Nothing
Feature selection methods"
In familiar: End-to-End Automated Machine Learning and Model Evaluation
Feature selection methods in familiar measure variable importance in a
univariate or multivariate setting.
| method | tag | binomial | multinomial | continuous | count | survival |
|:------------|:------------|:-----:|:-----:|:-----:|:-----:|:-----:|
| correlation |
| Pearson's r | pearson | | | × | × | × |
| Spearman's ρ | spearman | | | × | × | × |
| Kendall's τ | kendall | | | × | × | × |
| concordance |
| concordance^a^ | concordance | × | × | × | × | × |
| CORElearn |
| information gain ratio | gain_ratio | × | × | | | |
| gini-index | gini | × | × | | | |
| minimum description length | mdl | × | × | | | |
| ReliefF with exponential weighting of distance ranks | relieff_exp_rank | × | × | × | × | |
| mutual information|
| mutual information maximisation | mim | × | × | × | × | × |
| mutual information features selection | mifs | × | × | × | × | × |
| minimum redundancy maximum relevance | mrmr | × | × | × | × | × |
| univariate regression |
| univariate regression | univariate_regression | × | × | × | × | × |
| multivariate regression|
| multivariate regression | multivariate_regression | × | × | × | × | × |
| lasso regression|
| general^a^ | lasso | × | × | × | × | × |
| logistic | lasso_binomial | × | | | | |
| multi-logistic | lasso_multinomial | | × | | | |
| normal (gaussian) | lasso_gaussian | | | × | | |
| poisson | lasso_poisson | | | | × | |
| cox | lasso_cox | | | | | × |
| ridge regression|
| general^a^ | ridge | × | × | × | × | × |
| logistic | ridge_binomial | × | | | | |
| multi-logistic | ridge_multinomial | | × | | | |
| normal (gaussian) | ridge_gaussian | | | × | | |
| poisson | ridge_poisson | | | | × | |
| cox | ridge_cox | | | | | × |
| elastic net regression|
| general^a,b^ | elastic_net | × | × | × | × | × |
| logistic^b^ | elastic_net_binomial | × | | | | |
| multi-logistic^b^ | elastic_net_multinomial | | × | | | |
| normal (gaussian)^b^ | elastic_net_gaussian | | | × | | |
| poisson^b^ | elastic_net_poisson | | | | × | |
| cox^b^ | elastic_net_cox | | | | | × |
| random forest (RFSRC) variable importance|
| permutation^b^ | random_forest_permutation | × | × | × | × | × |
| permutation (unoptimised) | random_forest_permutation_default | × | × | × | × | × |
| minimum depth^b^ | random_forest_minimum_depth | × | × | × | × | × |
| minimum depth (unoptimised) | random_forest_minimum_depth_default | × | × | × | × | × |
| variable hunting^b^ | random_forest_variable_hunting | × | × | × | × | × |
| variable hunting (unoptimised) | random_forest_variable_hunting_default | × | × | × | × | × |
| hold-out ^b^ | random_forest_holdout | × | × | × | × | × |
| hold-out (unoptimised) | random_forest_holdout_default | × | × | × | × | × |
| random forest (ranger) variable importance|
| permutation^b^ | random_forest_ranger_permutation | × | × | × | × | × |
| permutation (unoptimised) | random_forest_ranger_permutation_default | × | × | × | × | × |
| hold-out permutation^b^ | random_forest_ranger_holdout_permutation | × | × | × | × | × |
| hold-out perm. (unoptimised) |random_forest_ranger_holdout_permutation_default | × | × | × | × | × |
| impurity^b^ | random_forest_ranger_impurity | × | × | × | × | x |
| impurity (unoptimised) | random_forest_ranger_impurity_default | × | × | × | × | x |
| special methods|
| no selection | none | × | × | × | × | × |
| random selection | random | × | × | × | × | × |
| signature only | signature_only | × | × | × | × | × |
Table: Overview of feature selection methods. ^a^ This is a general method where
an appropriate specific method will be chosen, or multiple distributions or
linking families are tested in an attempt to find the best option. ^b^ This
method requires hyperparameter optimisation.
Configuration options
Feature selection methods and related options can be provided within the
feature_selection tag in the xml file or as function argument.
| tag / argument | description | default |
|:----------:|:-------------------------|:-----------:|
| fs_method | The desired feature selection method. Multiple selection methods may be provided at the same time. This setting has no default and must be provided. | -- (required) |
| fs_method_parameter | Several feature selection methods have hyperparameters that can be set and/or optimised. | -- (optional) |
| vimp_aggregation_method | The aggregation method used to aggregate feature ranks over different bootstraps. | borda |
| vimp_aggregation_rank_threshold | Several aggregation methods count features if they have a rank below the threshold, i.e. are among the most important features. If NULL, a dynamic threshold is decided through Otsu-thresholding. | NULL |
| parallel_feature_selection | Enables parallel processing for feature selection. Ignored if parallel=FALSE. | TRUE |
Providing parameters for feature selection
Some of the feature selection methods, notably those based on random forests and
(penalised) regression, have parameters that can be set. These parameters are
mentioned under the respective entries in the [Overview of feature selection
methods] section. Moreover, some of these parameters are model parameters. In
this case, these parameters are optimised using hyperparameter optimisation,
which is described in the learning algorithms and hyperparameter optimisation vignette.
The syntax for such parameters is the same as for hyperparameter optimisation.
For the multivariate_regression feature selection method the alpha parameter
(which determines feature drop-out during forward selection) may be provided as
follows using the configuration file:
<fs_method_parameter>
<multivariate_regression>
<alpha>0.05</alpha>
</multivariate_regression>
</fs_method_parameter>
Or as a nested list passed as the fs_method_parameter argument to summon_familiar:
fs_method_parameter = list("multivariate_regression"=list("alpha"=0.05))
Overview of feature selection methods
The feature selection methods implemented in familiar are described in more
detail in this section.
Correlation methods
Correlation methods determine variable importance by assessing the correlation
between a feature and the outcome of interest. High (anti-)correlation indicates
an important feature, whereas low (anti-)correlation indicates that a feature is
not directly related to the outcome. Correlation-based variable importance is
determined using the cor function of the stats package that is part of the R
core distribution [@rcore2018].
Three correlation coefficients can be computed:
pearson: Pearson’s $r$spearman: Spearman’s $\rho$kendall: Kendall’s $\tau$
To compute correlation of features with survival outcomes, only samples with an
event are considered.
Concordance methods
Concordance methods assess how well the ordering of feature values corresponds
to the ordering of the outcome. The method internally refers to the gini
method for binomial and multinomial outcomes and to the kendall method for
continuous and count outcomes. For survival outcomes, concordance is measured
using the concordance_index.
CORElearn methods
Familiar provides an interface to several feature selection methods implemented
in the CORElearn package. These methods are the Information Gain Ratio
(gain_ratio), the Gini-index (gini), Minimum Description Length (mdl) and
ReliefF and rReliefF with exponential distance rank weighting
(relieff_exp_rank).
Mutual information-based methods
Mutual information $I$ is a measure of interdependency between two variables $x$
and $y$. In the context of feature selection, $x$ is a feature vector and $y$ is
the outcome vector.
Computing mutual information requires that probability distributions of $x$ and
$y$ are known. In practice we don't know either one. For categorical $x$ and $y$
we can use the sample estimates instead. For continuous or mixed data, the
situation is more complex.
In familiar we therefore use the following three approaches to compute mutual
information:
-
For binomial and multinomial outcomes mutual information is computed using
sample estimates. In case of continuous $x$, these are discretised into
$\lceil 2 n^{1/3} \rceil$ bins, with $n$ the number of samples, after which
computation is conducted as if $x$ was a categorical variable.
-
For continuous and count outcomes, we use the approximation proposed by De
Jay et al. after Gel'fand and Yaglom [@Gelfand1959-de; @De_Jay2013-yl]:
$I = -0.5 \log(1 - \rho(x,y)^2 + \epsilon)$, with $\rho(x,y)$ Spearman's
correlation coefficient and $\epsilon$ a small positive number to prevent
$\log(0)$.
-
For survival outcomes the second method is adapted for use with a concordance
index: $I = -0.5 \log(1 - (2 * (ci-0.5))^2 + \epsilon)$, with $ci$ the
concordance index.
We opted to adapt the approach based on the outcome type as this ensures that a
single consistent approach is used to assess all feature data in an analysis,
thus making results comparable.
Mutual information maximisation
The mim method is a univariate method that ranks each feature by its mutual
information with the outcome.
Mutual information feature selection
Mutual information feature selection (MIFS) finds a feature set that maximises
mutual information [@Battiti1994-ja]. This is done using forward selection. As
in mutual information maximisation, mutual information $I_{y,j}$ between each
feature and the outcome is computed. Starting from a potential pool of all
features, the feature with the highest mutual information is selected and
removed from the pool.
The rest proceeds iteratively. The mutual information $I_{s,1j}$ between
the previously selected feature and the remaining features is computed. This
mutual information is also called redundancy. The feature with the highest
mutual information with the outcome and least redundancy (i.e. maximum
$I_{y,j} - I_{s,1j}$) is selected next, and removed from the pool of remaining
features. Then the mutual information $I_{s,2j}$ between this feature and
remaining features is computed, and the feature that maximises
$I_{y,j} - I{s,1j} - I_{s,2j}$ is selected, and so forth.
The iterative process stops if there is no feature $j$ for which
$I_{y,j} - \sum_{i\in S} I_{s,ij} > 0$, with $S$ being the subset of selected
features, or all features have been exhausted.
To reduce the number of required computations, the implementation in familiar
actively filters out any feature $j$ for which
$I_{y,j} - \sum_{i\in S} I_{s,ij} \leq 0$ at the earliest instance, as the
$\sum_{i\in S} I_{s,ij}$ term will monotonously increase.
Minimum redundancy maximum relevance
Minimum redundancy maximum relevance (mRMR) feature selection is similar to
MIFS but differs in the way redundancy is used during optimisation
[@Peng2005-oo]. Whereas for MIFS the optimisation criterion is
$I_{y,j} - \sum_{i\in S} I_{s,ij}$, in mRMR the optimisation criterion is
$I_{y,j} - \frac{1} {\left| S \right|} \sum_{i\in S} I_{s,ij}$,
with $\left| S \right|$ the number of features already selected.
Unlike in MIFS, the $\frac{1}{\left|S\right|}\sum_{i\in S}I_{s,ij}$ term is not
monotonically increasing. Consequently, features cannot be safely filtered.
To limit computational complexity, we still remove features for which
$I_{y,j} - \frac{1} {\left| S \right| + 3} \sum_{i\in S} I_{s,ij} \leq 0$,
as such features are unlikely to be selected.
Univariate and multivariate regression methods
Univariate and multivariate regression perform feature selection by performing
regression using a feature or set of features as predictors. The performance of
the regression model is then measured using a metric. Training and testing of
regression models are repeated multiple times using bootstraps. For each
bootstrap, the in-bag samples are used for training and the out-of-bag samples
are using for testing.
This also defines the parameters of both methods, which are shown in the table
below.
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| regression learner | learner | dependent on outcome | no | Any generalised linear regression model from the learning algorithms and hyperparameter optimisation vignette can be selected. Default values are glm_logistic for binomial, glm_multinomial for multinomial, glm_gaussian for continuous, glm_poisson for count, and cox for survival outcomes. |
| performance metric | metric | dependent on outcome | no | Any metric from the performance metrics vignette can be selected. Default values are auc_roc for binomial and multinomial, mse for continuous, msle for count and concordance_index for survival outcomes |
| number of bootstraps | n_bootstrap | $\mathbb{Z} \in \left[1, \infty\right)$ | no | The default value is $10$. |
| drop-out alpha level | alpha | $\mathbb{R} \in \left[0, 1\right]$ | no | The default value is $0.05$. Only used in multivariate regression. |
Univariate regression
In the univariate regression method, a regression model is built with each
feature separately using the in-bag data of the bootstrap. Then this model is
evaluated using the metric, expressed using an objective representation (see
computing the objective score in the learning algorithms and hyperparameter optimisation vignette). The
objective representation $s^$ is computed on both in-bag (IB) and out-of-bag
(OOB) data. Subsequently the balanced objective score $f$ is computed:
$f=s^{OOB} - \left|s^_{OOB}-s^{IB}\right|$.
The objective score $f$ is subsequently averaged over all bootstraps to obtain
the variable importance of a feature.
Multivariate regression
The procedure described for univariate regression forms the first step in
multivariate regression. The rest follows forward selection. The most
important feature is assigned to the subset of selected features and removed
from the set of available features. Separate regression models are then built
with each remaining feature and all the feature(s) in the selected feature
subset as predictors. Thus, the subset of selected features iteratively increases
in size until no features are remaining or the objective score no longer
increases.
To limit mostly redundant computation, features that are unlikely to be selected
are actively removed. To do so, the standard deviation of the objective score
over the bootstraps is computed for each feature. The (one-sided, upper-tail)
quantile $q$ corresponding to the alpha-level indicated by parameter alpha is
subsequently computed. If the obtained mean objective score is $q$ standard
deviations or more below the best objective score, the feature is removed.
Lasso, ridge and elastic net regression
Penalised regression is also a form of feature selection, as it selects an
'optimal' set of features to create a regression model. As features are usually
normalised as part of pre-processing, the magnitude of each coefficient can be
interpreted as its importance. All three shrinkage methods are implemented using
the glmnet package [@Hastie2009-ed; @Simon2011-ih].
Only elastic net regression has a model hyperparameter that requires
optimisation, but other parameters may be set as well, as shown in the table
below:
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| family | family | gaussian, binomial, poisson, multinomial, cox | continuous outcomes | For continuous outcomes gaussian and poisson may be tested. The family is not optimised when it is specified, e.g. lasso_gaussian. For other outcomes only one applicable family exists.|
| elastic net penalty | alpha | $\mathbb{R} \in \left[0,1\right]$ | elastic net | This penalty is fixed for ridge regression (alpha = 0) and lasso (alpha = 1). |
| optimal lambda | lambda_min | lambda.1se, lambda.min | no | Default is lambda.min.|
| number of CV folds | n_folds | $\mathbb{Z} \in \left[3,n\right]$ | no | Default is $3$ if $n<30$, $\lfloor n/10\rfloor$ if $30\leq n \leq 200$ and $20$ if $n>200$.|
| normalisation | normalise | FALSE, TRUE | no | Default is FALSE, as normalisation is part of pre-processing in familiar.|
Random forest-based methods
Several feature selection methods are based on random forests. All these methods
require that a random forest model exists. Hence, familiar will train a random
forest based on the training data. Random forest learners have a set of
hyperparameters that are optimised prior to training, and these make up most of
the method-specific parameters. These parameters, which are slightly different
for ranger-based and randomForestSRC-based methods, are shown below.
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| number of trees | n_tree | $\mathbb{Z} \in \left[0,\infty\right)$ | yes | This parameter is expressed on the $\log_{2}$ scale, i.e. the actual input value will be $2^\texttt{n_tree}$ [@Oshiro2012-mq]. The default range is $\left[4, 10\right]$.|
| subsampling fraction | sample_size | $\mathbb{R} \in \left(0, 1.0\right]$ | yes | Fraction of available data that is used for to create a single tree. The default range is $\left[2 / m, 1.0\right]$, with $m$ the number of samples. |
| number of features at each node | m_try | $\mathbb{R} \in \left[0.0, 1.0\right]$ | yes | Familiar ensures that there is always at least one candidate feature. |
| node size | node_size | $\mathbb{Z} \in \left[1, \infty\right)$ | yes | Minimum number of unique samples in terminal nodes. The default range is $\left[5, \lfloor m / 3\rfloor\right]$, with $m$ the number of samples.|
| maximum tree depth | tree_depth | $\mathbb{Z} \in \left[1,\infty\right)$ | yes | Maximum depth to which trees are allowed to grow. The default range is $\left[1, 10\right]$. |
| number of split points | n_split | $\mathbb{Z} \in \left[0, \infty\right)$ | no | By default, splitting is deterministic and has one split point ($0$).|
| splitting rule (randomForestSRC only) | split_rule | gini, auc, entropy, mse, quantile.regr, la.quantile.regr, logrank, logrankscore, bs.gradient | no | Default splitting rules are gini for binomial and multinonial outcomes, mse for continuous and count outcomes and logrank for survival outcomes.|
| splitting rule (ranger only) | split_rule | gini, hellinger, extratrees, beta, variance, logrank, C, maxstat| no | Default splitting rules are gini for binomial and multinomial outcomes and maxstat for continuous, count and survival outcomes.|
| significance split threshold (ranger only) | alpha | $\mathbb{R} \in \left(0.0, 1.0\right]$ | maxstat | Minimum significance level for further splitting. The default range is $\left[10^{-6}, 1.0\right]$ |
| variable hunting cross-validation folds | fs_vh_fold | $\mathbb{Z} \in \left[2, \infty\right)$ | no | Number of cross-validation folds for the random_forest_variable_hunting method. The default is $5$. |
| variable hunting step size | fs_vh_step_size | $\mathbb{Z} \in \left[1, \infty\right)$ | no | Step size for the random_forest_variable_hunting method. The default is $1$. |
| variable hunting iterations | fs_vh_n_rep | $\mathbb{Z} \in \left[1, \infty\right)$ | no | Number of Monte Carlo iterations for the random_forest_variable_hunting method. The default is $50$. |
The unoptimised methods do not require hyperparameter optimisation, and use
default values from the ranger and randomForestSRC. For
random_forest_variable_hunting_defaultthe fs_vh_fold, fs_vh_step_size and
fs_vh_n_rep parameters can be set.
Permutation importance
The permutation importance method is implemented by random_forest_permutation
and random_forest_permutation_default (randomForestSRC package) and
random_forest_ranger_permutation and
random_forest_ranger_permutation_default (ranger package). In short, this
method functions as follows [Ishwaran2007-va]. As usual, each tree in the random
forest is constructed using the in-bag samples of a bootstrap of the data. The
predictive performance of each model is first measured using the out-of-bag
data. Subsequently, the out-of-bag instances for each feature are randomly
permuted, and predictive performance is assessed again. The difference between
the normal performance and the permuted performance is used as a measure of the
variable importance. For important features, this difference is large, whereas
for irrelevant features the difference is negligible or even negative.
Holdout permutation importance
This variant on permutation importance
(random_forest_ranger_holdout_permutation and
random_forest_ranger_holdout_permutation_default) is implemented using
ranger::holdoutRF. Instead of using out-of-bag to compute feature importance,
two cross-validation folds are used. A random forest is trained on either fold,
and variable importance determined on the other [@Janitza2018-kl].
The hold-out variable importance method implemented in the randomForestSRC
package (random_forest_holdout and random_forest_holdout_default) is
implemented using randomForestSRC::holdout.vimp. It is similar to the previous
variant, but does not cross-validation folds. Instead, out-of-bag prediction
errors for models trained with and without each feature are compared.
Minimum depth variable selection
Important features tend to appear closer to the root of trees in random forests.
Therefore, the position of each feature within a tree is assessed in minimum
depth variable selection [@Ishwaran2010-zv].
Variable hunting
Variable hunting is implemented using the variable hunting algorithm implemented
in randomForestSRC. Ishwaran suggest using it when minimum depth variable
selection leads to high computational load, or a larger set of variables should
be found [@Ishwaran2010-zv].
The variable hunting selection method has several parameters which can be set.
Impurity importance
At each node, the data is split into (two) subsets, which connects to two
branches. After splitting, each single subset is purer than the parent dataset.
As a concrete example, in regression problems the variance of each of the
subsets is lower than that of the data prior to splitting. The decrease in
variance specifically, or the decrease of impurity generally, is then used to
assess feature importance.
familiar uses the impurity_corrected importance measure, which is unbiased
to the number of split points of a feature and its distribution
[@Nembrini2018-ay].
Special methods
Familiar offers several methods that are special in that they are not feature
selection methods in the sense that they determine a variable importance that
can be used for establishing feature rankings.
No feature selection
As the name suggests, the none method avoids feature selection altogether. All
features are passed into a model. Feature order is randomly shuffled prior to
building a model to avoid influence of the provided feature order.
Random feature selection
The random method randomly draws features prior to model building. It does not
assign a random variable importance to a feature. New features are drawn each
time a model is built. All features are available for the draw, but only $m$
features are drawn. Here $m$ is the signature size that is usually optimised by
hyperparameter optimisation.
Signature only
When configuring familiar, any number of features can be set as a model
signature using the signature configuration parameter. However, more
features may be added to this signature through feature selection. To make sure
that only the provided features enter a model, the signature_only method may
be used.
Aggregating variable importance
In case of feature selection or modelling in the presence of resampling (e.g.
bootstraps), the ranks of features may need to be aggregated across the
different instances [@Wald2012-zk]. The rank aggregation methods shown in the
table below can be used for this purpose. Several methods require a threshold to
indicate the size of the set of most highly ranked features, which can be set by
specifying the vimp_aggregation_rank_threshold configuration parameter.
| aggregation method | tag | comments |
|:--------------|:--------|:----------:|
| none | none | |
| mean rank | mean | |
| median rank | median | |
| best rank | best | |
| worst rank | worst | |
| stability selection | stability | uses threshold |
| exponential selection | exponential | uses threshold |
| borda ranking | borda | |
| enhanced borda ranking | enhanced_borda| uses threshold |
| truncated borda ranking | truncated_borda | uses threshold |
| enhanced truncated borda ranking | enhanced_truncated_borda | uses threshold |
Notation
Let $N$ be the number of ranking experiments that should be aggregated. Feature
$i$ for experiment $j$ of $N$ then has rank $r_{ij}$. A lower rank indicates a
more important feature. Some features may not receive a score during a ranking
experiment, for example for multivariate variable importance methods such as
lasso regression, or by use of a threshold $\tau$. This is designated by
$\delta_{ij}$, which is $0$ if the feature is absent, and $1$ if it is present.
In case a threshold is used, $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$
otherwise.
Thus, for each experiment $m_j = \sum^M_{i=1} \delta_{ij}$ features are ranked,
out of $M$ features. $m_j$ is then also the maximum rank found in experiment
$j$.
Aggregating ranks for each feature results in an aggregate rank score $s_i$.
Features are subsequently ranked according to this method-specific score to
arrive at an aggregate feature rank $r_i$.
No rank aggregation
The none option does not aggregate ranks. Rather, scores are aggregated by
computing the average score of a feature over all experiments that contain it.
Ranks are then computed from the aggregated scores.
Mean rank aggregation
The mean rank aggregation method ranks features by computing the mean rank of a
feature across all experiments that contain it.
$$s_i = \frac{\sum^{N}{j=1} \delta{ij} r_{ij}}{\sum^{N}{j=1} \delta{ij}}$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in ascending order.
Median rank aggregation
The median rank aggregation method ranks features by computing the median rank
of a feature across all experiments that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{median}}(r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in ascending order.
Best rank aggregation
The best rank aggregation method ranks features by the best rank that a feature
has across all experiments that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{min}} (r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in ascending order.
Worst rank aggregation
The worst rank aggregation method ranks features by the worst rank that a
feature has across all instances that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{max}} (r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in ascending order.
Stability rank aggregation
The stability aggregation method ranks features by their occurrence within the
set of highly ranked features across all experiments. Our implementation
generalises the method originally proposed by Meinshausen and Bühlmann
[@Meinshausen2010-do].
This method uses threshold $\tau$ to designate the highly ranked features. Thus
$\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
The aggregate rank score is computed as:
$$s_i = \frac{1}{N} \sum^N_{j=1} \delta_{ij}$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order, as more commonly occurring features are considered
more important.
Exponential rank aggregation
The exponential aggregation method ranks features by the sum of the negative
exponentials of their normalised ranks in instances where they occur within the
set of highly ranked features. This method was originally suggested by Haury et
al. [@Haury2011-zd].
This method uses threshold $\tau$ to designate the highly ranked features. Thus
$\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \sum^N_{j=1} \delta_{ij} \exp({-r_{ij} / \tau)}$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order.
Borda rank aggregation
Borda rank aggregation ranks a feature by the sum of normalised ranks (the borda
score) across all experiments that contain it. In case every experiment contains all
features, the result is equivalent to the mean aggregation method [@Wald2012-zk].
$$s_i = \sum^N_{j=1} \frac{m_j - r_{ij} + 1}{m_j}$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order.
Enhanced borda rank aggregation
Enhanced borda rank aggregation combines borda rank aggregation with stability
rank aggregation. The borda score is multiplied by the occurrence of the feature
within the set of highly ranked features across all experiments [@Wald2012-zk].
This method uses threshold $\tau$ to designate the highly ranked features for
the purpose of computing the occurrence. Thus $\delta_{ij} = 1$ if
$r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \left( \frac{1}{N} \sum^N_{j=1} \delta_{ij} \right) \left( \sum^N_{j=1}
\frac{m_j - r_{ij} + 1}{m_j} \right)$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order.
Truncated borda rank aggregation
Truncated borda rank aggregation is borda rank aggregation performed with only
the set of most highly ranked features in each instance.
This method uses threshold $\tau$ to designate the highly ranked features. Thus
$\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \sum^N_{j=1} \delta_{ij} \frac{\tau - r_{ij} + 1}{\tau}$$
Note that compared to the borda method, the number of ranked features in an
experiment $m_j$ is replaced by threshold $\tau$.
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order.
Truncated enhanced borda rank aggregation
Truncated enhanced borda rank aggregation is enhanced borda aggregation
performed with only the set of most highly ranked features in each experiment.
This method uses threshold $\tau$ to designate the highly ranked features. Thus
$\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \left( \frac{1}{N} \sum^N_{j=1} \delta_{ij} \right) \left( \sum^N_{j=1}
\delta_{ij} \frac{\tau - r_{ij} + 1}{\tau} \right)$$
The aggregate rank of features is then determined by sorting aggregate scores
$s_i$ in descending order.
References
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Feature selection methods in familiar measure variable importance in a univariate or multivariate setting.
| method | tag | binomial | multinomial | continuous | count | survival |
|:------------|:------------|:-----:|:-----:|:-----:|:-----:|:-----:|
| correlation |
| Pearson's r | pearson | | | × | × | × |
| Spearman's ρ | spearman | | | × | × | × |
| Kendall's τ | kendall | | | × | × | × |
| concordance |
| concordance^a^ | concordance | × | × | × | × | × |
| CORElearn |
| information gain ratio | gain_ratio | × | × | | | |
| gini-index | gini | × | × | | | |
| minimum description length | mdl | × | × | | | |
| ReliefF with exponential weighting of distance ranks | relieff_exp_rank | × | × | × | × | |
| mutual information|
| mutual information maximisation | mim | × | × | × | × | × |
| mutual information features selection | mifs | × | × | × | × | × |
| minimum redundancy maximum relevance | mrmr | × | × | × | × | × |
| univariate regression |
| univariate regression | univariate_regression | × | × | × | × | × |
| multivariate regression|
| multivariate regression | multivariate_regression | × | × | × | × | × |
| lasso regression|
| general^a^ | lasso | × | × | × | × | × |
| logistic | lasso_binomial | × | | | | |
| multi-logistic | lasso_multinomial | | × | | | |
| normal (gaussian) | lasso_gaussian | | | × | | |
| poisson | lasso_poisson | | | | × | |
| cox | lasso_cox | | | | | × |
| ridge regression|
| general^a^ | ridge | × | × | × | × | × |
| logistic | ridge_binomial | × | | | | |
| multi-logistic | ridge_multinomial | | × | | | |
| normal (gaussian) | ridge_gaussian | | | × | | |
| poisson | ridge_poisson | | | | × | |
| cox | ridge_cox | | | | | × |
| elastic net regression|
| general^a,b^ | elastic_net | × | × | × | × | × |
| logistic^b^ | elastic_net_binomial | × | | | | |
| multi-logistic^b^ | elastic_net_multinomial | | × | | | |
| normal (gaussian)^b^ | elastic_net_gaussian | | | × | | |
| poisson^b^ | elastic_net_poisson | | | | × | |
| cox^b^ | elastic_net_cox | | | | | × |
| random forest (RFSRC) variable importance|
| permutation^b^ | random_forest_permutation | × | × | × | × | × |
| permutation (unoptimised) | random_forest_permutation_default | × | × | × | × | × |
| minimum depth^b^ | random_forest_minimum_depth | × | × | × | × | × |
| minimum depth (unoptimised) | random_forest_minimum_depth_default | × | × | × | × | × |
| variable hunting^b^ | random_forest_variable_hunting | × | × | × | × | × |
| variable hunting (unoptimised) | random_forest_variable_hunting_default | × | × | × | × | × |
| hold-out ^b^ | random_forest_holdout | × | × | × | × | × |
| hold-out (unoptimised) | random_forest_holdout_default | × | × | × | × | × |
| random forest (ranger) variable importance|
| permutation^b^ | random_forest_ranger_permutation | × | × | × | × | × |
| permutation (unoptimised) | random_forest_ranger_permutation_default | × | × | × | × | × |
| hold-out permutation^b^ | random_forest_ranger_holdout_permutation | × | × | × | × | × |
| hold-out perm. (unoptimised) |random_forest_ranger_holdout_permutation_default | × | × | × | × | × |
| impurity^b^ | random_forest_ranger_impurity | × | × | × | × | x |
| impurity (unoptimised) | random_forest_ranger_impurity_default | × | × | × | × | x |
| special methods|
| no selection | none | × | × | × | × | × |
| random selection | random | × | × | × | × | × |
| signature only | signature_only | × | × | × | × | × |
Table: Overview of feature selection methods. ^a^ This is a general method where an appropriate specific method will be chosen, or multiple distributions or linking families are tested in an attempt to find the best option. ^b^ This method requires hyperparameter optimisation.
Configuration options
Feature selection methods and related options can be provided within the
feature_selection tag in the xml file or as function argument.
| tag / argument | description | default |
|:----------:|:-------------------------|:-----------:|
| fs_method | The desired feature selection method. Multiple selection methods may be provided at the same time. This setting has no default and must be provided. | -- (required) |
| fs_method_parameter | Several feature selection methods have hyperparameters that can be set and/or optimised. | -- (optional) |
| vimp_aggregation_method | The aggregation method used to aggregate feature ranks over different bootstraps. | borda |
| vimp_aggregation_rank_threshold | Several aggregation methods count features if they have a rank below the threshold, i.e. are among the most important features. If NULL, a dynamic threshold is decided through Otsu-thresholding. | NULL |
| parallel_feature_selection | Enables parallel processing for feature selection. Ignored if parallel=FALSE. | TRUE |
Providing parameters for feature selection
Some of the feature selection methods, notably those based on random forests and (penalised) regression, have parameters that can be set. These parameters are mentioned under the respective entries in the [Overview of feature selection methods] section. Moreover, some of these parameters are model parameters. In this case, these parameters are optimised using hyperparameter optimisation, which is described in the learning algorithms and hyperparameter optimisation vignette.
The syntax for such parameters is the same as for hyperparameter optimisation.
For the multivariate_regression feature selection method the alpha parameter
(which determines feature drop-out during forward selection) may be provided as
follows using the configuration file:
<fs_method_parameter> <multivariate_regression> <alpha>0.05</alpha> </multivariate_regression> </fs_method_parameter>
Or as a nested list passed as the fs_method_parameter argument to summon_familiar:
fs_method_parameter = list("multivariate_regression"=list("alpha"=0.05))
Overview of feature selection methods
The feature selection methods implemented in familiar are described in more detail in this section.
Correlation methods
Correlation methods determine variable importance by assessing the correlation
between a feature and the outcome of interest. High (anti-)correlation indicates
an important feature, whereas low (anti-)correlation indicates that a feature is
not directly related to the outcome. Correlation-based variable importance is
determined using the cor function of the stats package that is part of the R
core distribution [@rcore2018].
Three correlation coefficients can be computed:
pearson: Pearson’s $r$spearman: Spearman’s $\rho$kendall: Kendall’s $\tau$
To compute correlation of features with survival outcomes, only samples with an event are considered.
Concordance methods
Concordance methods assess how well the ordering of feature values corresponds
to the ordering of the outcome. The method internally refers to the gini
method for binomial and multinomial outcomes and to the kendall method for
continuous and count outcomes. For survival outcomes, concordance is measured
using the concordance_index.
CORElearn methods
Familiar provides an interface to several feature selection methods implemented
in the CORElearn package. These methods are the Information Gain Ratio
(gain_ratio), the Gini-index (gini), Minimum Description Length (mdl) and
ReliefF and rReliefF with exponential distance rank weighting
(relieff_exp_rank).
Mutual information-based methods
Mutual information $I$ is a measure of interdependency between two variables $x$ and $y$. In the context of feature selection, $x$ is a feature vector and $y$ is the outcome vector.
Computing mutual information requires that probability distributions of $x$ and $y$ are known. In practice we don't know either one. For categorical $x$ and $y$ we can use the sample estimates instead. For continuous or mixed data, the situation is more complex.
In familiar we therefore use the following three approaches to compute mutual information:
-
For binomial and multinomial outcomes mutual information is computed using sample estimates. In case of continuous $x$, these are discretised into $\lceil 2 n^{1/3} \rceil$ bins, with $n$ the number of samples, after which computation is conducted as if $x$ was a categorical variable.
-
For continuous and count outcomes, we use the approximation proposed by De Jay et al. after Gel'fand and Yaglom [@Gelfand1959-de; @De_Jay2013-yl]: $I = -0.5 \log(1 - \rho(x,y)^2 + \epsilon)$, with $\rho(x,y)$ Spearman's correlation coefficient and $\epsilon$ a small positive number to prevent $\log(0)$.
-
For survival outcomes the second method is adapted for use with a concordance index: $I = -0.5 \log(1 - (2 * (ci-0.5))^2 + \epsilon)$, with $ci$ the concordance index.
We opted to adapt the approach based on the outcome type as this ensures that a single consistent approach is used to assess all feature data in an analysis, thus making results comparable.
Mutual information maximisation
The mim method is a univariate method that ranks each feature by its mutual
information with the outcome.
Mutual information feature selection
Mutual information feature selection (MIFS) finds a feature set that maximises mutual information [@Battiti1994-ja]. This is done using forward selection. As in mutual information maximisation, mutual information $I_{y,j}$ between each feature and the outcome is computed. Starting from a potential pool of all features, the feature with the highest mutual information is selected and removed from the pool.
The rest proceeds iteratively. The mutual information $I_{s,1j}$ between the previously selected feature and the remaining features is computed. This mutual information is also called redundancy. The feature with the highest mutual information with the outcome and least redundancy (i.e. maximum $I_{y,j} - I_{s,1j}$) is selected next, and removed from the pool of remaining features. Then the mutual information $I_{s,2j}$ between this feature and remaining features is computed, and the feature that maximises $I_{y,j} - I{s,1j} - I_{s,2j}$ is selected, and so forth.
The iterative process stops if there is no feature $j$ for which $I_{y,j} - \sum_{i\in S} I_{s,ij} > 0$, with $S$ being the subset of selected features, or all features have been exhausted.
To reduce the number of required computations, the implementation in familiar actively filters out any feature $j$ for which $I_{y,j} - \sum_{i\in S} I_{s,ij} \leq 0$ at the earliest instance, as the $\sum_{i\in S} I_{s,ij}$ term will monotonously increase.
Minimum redundancy maximum relevance
Minimum redundancy maximum relevance (mRMR) feature selection is similar to MIFS but differs in the way redundancy is used during optimisation [@Peng2005-oo]. Whereas for MIFS the optimisation criterion is $I_{y,j} - \sum_{i\in S} I_{s,ij}$, in mRMR the optimisation criterion is $I_{y,j} - \frac{1} {\left| S \right|} \sum_{i\in S} I_{s,ij}$, with $\left| S \right|$ the number of features already selected.
Unlike in MIFS, the $\frac{1}{\left|S\right|}\sum_{i\in S}I_{s,ij}$ term is not monotonically increasing. Consequently, features cannot be safely filtered. To limit computational complexity, we still remove features for which $I_{y,j} - \frac{1} {\left| S \right| + 3} \sum_{i\in S} I_{s,ij} \leq 0$, as such features are unlikely to be selected.
Univariate and multivariate regression methods
Univariate and multivariate regression perform feature selection by performing regression using a feature or set of features as predictors. The performance of the regression model is then measured using a metric. Training and testing of regression models are repeated multiple times using bootstraps. For each bootstrap, the in-bag samples are used for training and the out-of-bag samples are using for testing.
This also defines the parameters of both methods, which are shown in the table below.
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| regression learner | learner | dependent on outcome | no | Any generalised linear regression model from the learning algorithms and hyperparameter optimisation vignette can be selected. Default values are glm_logistic for binomial, glm_multinomial for multinomial, glm_gaussian for continuous, glm_poisson for count, and cox for survival outcomes. |
| performance metric | metric | dependent on outcome | no | Any metric from the performance metrics vignette can be selected. Default values are auc_roc for binomial and multinomial, mse for continuous, msle for count and concordance_index for survival outcomes |
| number of bootstraps | n_bootstrap | $\mathbb{Z} \in \left[1, \infty\right)$ | no | The default value is $10$. |
| drop-out alpha level | alpha | $\mathbb{R} \in \left[0, 1\right]$ | no | The default value is $0.05$. Only used in multivariate regression. |
Univariate regression
In the univariate regression method, a regression model is built with each
feature separately using the in-bag data of the bootstrap. Then this model is
evaluated using the metric, expressed using an objective representation (see
computing the objective score in the learning algorithms and hyperparameter optimisation vignette). The
objective representation $s^$ is computed on both in-bag (IB) and out-of-bag
(OOB) data. Subsequently the balanced objective score $f$ is computed:
$f=s^{OOB} - \left|s^_{OOB}-s^{IB}\right|$.
The objective score $f$ is subsequently averaged over all bootstraps to obtain the variable importance of a feature.
Multivariate regression
The procedure described for univariate regression forms the first step in multivariate regression. The rest follows forward selection. The most important feature is assigned to the subset of selected features and removed from the set of available features. Separate regression models are then built with each remaining feature and all the feature(s) in the selected feature subset as predictors. Thus, the subset of selected features iteratively increases in size until no features are remaining or the objective score no longer increases.
To limit mostly redundant computation, features that are unlikely to be selected
are actively removed. To do so, the standard deviation of the objective score
over the bootstraps is computed for each feature. The (one-sided, upper-tail)
quantile $q$ corresponding to the alpha-level indicated by parameter alpha is
subsequently computed. If the obtained mean objective score is $q$ standard
deviations or more below the best objective score, the feature is removed.
Lasso, ridge and elastic net regression
Penalised regression is also a form of feature selection, as it selects an
'optimal' set of features to create a regression model. As features are usually
normalised as part of pre-processing, the magnitude of each coefficient can be
interpreted as its importance. All three shrinkage methods are implemented using
the glmnet package [@Hastie2009-ed; @Simon2011-ih].
Only elastic net regression has a model hyperparameter that requires optimisation, but other parameters may be set as well, as shown in the table below:
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| family | family | gaussian, binomial, poisson, multinomial, cox | continuous outcomes | For continuous outcomes gaussian and poisson may be tested. The family is not optimised when it is specified, e.g. lasso_gaussian. For other outcomes only one applicable family exists.|
| elastic net penalty | alpha | $\mathbb{R} \in \left[0,1\right]$ | elastic net | This penalty is fixed for ridge regression (alpha = 0) and lasso (alpha = 1). |
| optimal lambda | lambda_min | lambda.1se, lambda.min | no | Default is lambda.min.|
| number of CV folds | n_folds | $\mathbb{Z} \in \left[3,n\right]$ | no | Default is $3$ if $n<30$, $\lfloor n/10\rfloor$ if $30\leq n \leq 200$ and $20$ if $n>200$.|
| normalisation | normalise | FALSE, TRUE | no | Default is FALSE, as normalisation is part of pre-processing in familiar.|
Random forest-based methods
Several feature selection methods are based on random forests. All these methods
require that a random forest model exists. Hence, familiar will train a random
forest based on the training data. Random forest learners have a set of
hyperparameters that are optimised prior to training, and these make up most of
the method-specific parameters. These parameters, which are slightly different
for ranger-based and randomForestSRC-based methods, are shown below.
| parameter | tag | values | optimised | comments |
|:--------------|:--------|:----------:|:----------:|:-------------|
| number of trees | n_tree | $\mathbb{Z} \in \left[0,\infty\right)$ | yes | This parameter is expressed on the $\log_{2}$ scale, i.e. the actual input value will be $2^\texttt{n_tree}$ [@Oshiro2012-mq]. The default range is $\left[4, 10\right]$.|
| subsampling fraction | sample_size | $\mathbb{R} \in \left(0, 1.0\right]$ | yes | Fraction of available data that is used for to create a single tree. The default range is $\left[2 / m, 1.0\right]$, with $m$ the number of samples. |
| number of features at each node | m_try | $\mathbb{R} \in \left[0.0, 1.0\right]$ | yes | Familiar ensures that there is always at least one candidate feature. |
| node size | node_size | $\mathbb{Z} \in \left[1, \infty\right)$ | yes | Minimum number of unique samples in terminal nodes. The default range is $\left[5, \lfloor m / 3\rfloor\right]$, with $m$ the number of samples.|
| maximum tree depth | tree_depth | $\mathbb{Z} \in \left[1,\infty\right)$ | yes | Maximum depth to which trees are allowed to grow. The default range is $\left[1, 10\right]$. |
| number of split points | n_split | $\mathbb{Z} \in \left[0, \infty\right)$ | no | By default, splitting is deterministic and has one split point ($0$).|
| splitting rule (randomForestSRC only) | split_rule | gini, auc, entropy, mse, quantile.regr, la.quantile.regr, logrank, logrankscore, bs.gradient | no | Default splitting rules are gini for binomial and multinonial outcomes, mse for continuous and count outcomes and logrank for survival outcomes.|
| splitting rule (ranger only) | split_rule | gini, hellinger, extratrees, beta, variance, logrank, C, maxstat| no | Default splitting rules are gini for binomial and multinomial outcomes and maxstat for continuous, count and survival outcomes.|
| significance split threshold (ranger only) | alpha | $\mathbb{R} \in \left(0.0, 1.0\right]$ | maxstat | Minimum significance level for further splitting. The default range is $\left[10^{-6}, 1.0\right]$ |
| variable hunting cross-validation folds | fs_vh_fold | $\mathbb{Z} \in \left[2, \infty\right)$ | no | Number of cross-validation folds for the random_forest_variable_hunting method. The default is $5$. |
| variable hunting step size | fs_vh_step_size | $\mathbb{Z} \in \left[1, \infty\right)$ | no | Step size for the random_forest_variable_hunting method. The default is $1$. |
| variable hunting iterations | fs_vh_n_rep | $\mathbb{Z} \in \left[1, \infty\right)$ | no | Number of Monte Carlo iterations for the random_forest_variable_hunting method. The default is $50$. |
The unoptimised methods do not require hyperparameter optimisation, and use
default values from the ranger and randomForestSRC. For
random_forest_variable_hunting_defaultthe fs_vh_fold, fs_vh_step_size and
fs_vh_n_rep parameters can be set.
Permutation importance
The permutation importance method is implemented by random_forest_permutation
and random_forest_permutation_default (randomForestSRC package) and
random_forest_ranger_permutation and
random_forest_ranger_permutation_default (ranger package). In short, this
method functions as follows [Ishwaran2007-va]. As usual, each tree in the random
forest is constructed using the in-bag samples of a bootstrap of the data. The
predictive performance of each model is first measured using the out-of-bag
data. Subsequently, the out-of-bag instances for each feature are randomly
permuted, and predictive performance is assessed again. The difference between
the normal performance and the permuted performance is used as a measure of the
variable importance. For important features, this difference is large, whereas
for irrelevant features the difference is negligible or even negative.
Holdout permutation importance
This variant on permutation importance
(random_forest_ranger_holdout_permutation and
random_forest_ranger_holdout_permutation_default) is implemented using
ranger::holdoutRF. Instead of using out-of-bag to compute feature importance,
two cross-validation folds are used. A random forest is trained on either fold,
and variable importance determined on the other [@Janitza2018-kl].
The hold-out variable importance method implemented in the randomForestSRC
package (random_forest_holdout and random_forest_holdout_default) is
implemented using randomForestSRC::holdout.vimp. It is similar to the previous
variant, but does not cross-validation folds. Instead, out-of-bag prediction
errors for models trained with and without each feature are compared.
Minimum depth variable selection
Important features tend to appear closer to the root of trees in random forests. Therefore, the position of each feature within a tree is assessed in minimum depth variable selection [@Ishwaran2010-zv].
Variable hunting
Variable hunting is implemented using the variable hunting algorithm implemented
in randomForestSRC. Ishwaran suggest using it when minimum depth variable
selection leads to high computational load, or a larger set of variables should
be found [@Ishwaran2010-zv].
The variable hunting selection method has several parameters which can be set.
Impurity importance
At each node, the data is split into (two) subsets, which connects to two branches. After splitting, each single subset is purer than the parent dataset. As a concrete example, in regression problems the variance of each of the subsets is lower than that of the data prior to splitting. The decrease in variance specifically, or the decrease of impurity generally, is then used to assess feature importance.
familiar uses the impurity_corrected importance measure, which is unbiased
to the number of split points of a feature and its distribution
[@Nembrini2018-ay].
Special methods
Familiar offers several methods that are special in that they are not feature selection methods in the sense that they determine a variable importance that can be used for establishing feature rankings.
No feature selection
As the name suggests, the none method avoids feature selection altogether. All
features are passed into a model. Feature order is randomly shuffled prior to
building a model to avoid influence of the provided feature order.
Random feature selection
The random method randomly draws features prior to model building. It does not
assign a random variable importance to a feature. New features are drawn each
time a model is built. All features are available for the draw, but only $m$
features are drawn. Here $m$ is the signature size that is usually optimised by
hyperparameter optimisation.
Signature only
When configuring familiar, any number of features can be set as a model
signature using the signature configuration parameter. However, more
features may be added to this signature through feature selection. To make sure
that only the provided features enter a model, the signature_only method may
be used.
Aggregating variable importance
In case of feature selection or modelling in the presence of resampling (e.g.
bootstraps), the ranks of features may need to be aggregated across the
different instances [@Wald2012-zk]. The rank aggregation methods shown in the
table below can be used for this purpose. Several methods require a threshold to
indicate the size of the set of most highly ranked features, which can be set by
specifying the vimp_aggregation_rank_threshold configuration parameter.
| aggregation method | tag | comments |
|:--------------|:--------|:----------:|
| none | none | |
| mean rank | mean | |
| median rank | median | |
| best rank | best | |
| worst rank | worst | |
| stability selection | stability | uses threshold |
| exponential selection | exponential | uses threshold |
| borda ranking | borda | |
| enhanced borda ranking | enhanced_borda| uses threshold |
| truncated borda ranking | truncated_borda | uses threshold |
| enhanced truncated borda ranking | enhanced_truncated_borda | uses threshold |
Notation
Let $N$ be the number of ranking experiments that should be aggregated. Feature $i$ for experiment $j$ of $N$ then has rank $r_{ij}$. A lower rank indicates a more important feature. Some features may not receive a score during a ranking experiment, for example for multivariate variable importance methods such as lasso regression, or by use of a threshold $\tau$. This is designated by $\delta_{ij}$, which is $0$ if the feature is absent, and $1$ if it is present.
In case a threshold is used, $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
Thus, for each experiment $m_j = \sum^M_{i=1} \delta_{ij}$ features are ranked, out of $M$ features. $m_j$ is then also the maximum rank found in experiment $j$.
Aggregating ranks for each feature results in an aggregate rank score $s_i$. Features are subsequently ranked according to this method-specific score to arrive at an aggregate feature rank $r_i$.
No rank aggregation
The none option does not aggregate ranks. Rather, scores are aggregated by
computing the average score of a feature over all experiments that contain it.
Ranks are then computed from the aggregated scores.
Mean rank aggregation
The mean rank aggregation method ranks features by computing the mean rank of a feature across all experiments that contain it.
$$s_i = \frac{\sum^{N}{j=1} \delta{ij} r_{ij}}{\sum^{N}{j=1} \delta{ij}}$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in ascending order.
Median rank aggregation
The median rank aggregation method ranks features by computing the median rank of a feature across all experiments that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{median}}(r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in ascending order.
Best rank aggregation
The best rank aggregation method ranks features by the best rank that a feature has across all experiments that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{min}} (r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in ascending order.
Worst rank aggregation
The worst rank aggregation method ranks features by the worst rank that a feature has across all instances that contain it.
$$s_i = \underset{j \in N, \, \delta_{ij}=1}{\textrm{max}} (r_{ij})$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in ascending order.
Stability rank aggregation
The stability aggregation method ranks features by their occurrence within the set of highly ranked features across all experiments. Our implementation generalises the method originally proposed by Meinshausen and Bühlmann [@Meinshausen2010-do].
This method uses threshold $\tau$ to designate the highly ranked features. Thus $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
The aggregate rank score is computed as:
$$s_i = \frac{1}{N} \sum^N_{j=1} \delta_{ij}$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order, as more commonly occurring features are considered more important.
Exponential rank aggregation
The exponential aggregation method ranks features by the sum of the negative exponentials of their normalised ranks in instances where they occur within the set of highly ranked features. This method was originally suggested by Haury et al. [@Haury2011-zd].
This method uses threshold $\tau$ to designate the highly ranked features. Thus $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \sum^N_{j=1} \delta_{ij} \exp({-r_{ij} / \tau)}$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order.
Borda rank aggregation
Borda rank aggregation ranks a feature by the sum of normalised ranks (the borda score) across all experiments that contain it. In case every experiment contains all features, the result is equivalent to the mean aggregation method [@Wald2012-zk].
$$s_i = \sum^N_{j=1} \frac{m_j - r_{ij} + 1}{m_j}$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order.
Enhanced borda rank aggregation
Enhanced borda rank aggregation combines borda rank aggregation with stability rank aggregation. The borda score is multiplied by the occurrence of the feature within the set of highly ranked features across all experiments [@Wald2012-zk].
This method uses threshold $\tau$ to designate the highly ranked features for the purpose of computing the occurrence. Thus $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \left( \frac{1}{N} \sum^N_{j=1} \delta_{ij} \right) \left( \sum^N_{j=1} \frac{m_j - r_{ij} + 1}{m_j} \right)$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order.
Truncated borda rank aggregation
Truncated borda rank aggregation is borda rank aggregation performed with only the set of most highly ranked features in each instance.
This method uses threshold $\tau$ to designate the highly ranked features. Thus $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \sum^N_{j=1} \delta_{ij} \frac{\tau - r_{ij} + 1}{\tau}$$
Note that compared to the borda method, the number of ranked features in an experiment $m_j$ is replaced by threshold $\tau$.
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order.
Truncated enhanced borda rank aggregation
Truncated enhanced borda rank aggregation is enhanced borda aggregation performed with only the set of most highly ranked features in each experiment.
This method uses threshold $\tau$ to designate the highly ranked features. Thus $\delta_{ij} = 1$ if $r_{ij} \leq \tau$, and $0$ otherwise.
$$s_i = \left( \frac{1}{N} \sum^N_{j=1} \delta_{ij} \right) \left( \sum^N_{j=1} \delta_{ij} \frac{\tau - r_{ij} + 1}{\tau} \right)$$
The aggregate rank of features is then determined by sorting aggregate scores $s_i$ in descending order.
References
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