dasvm: Discriminant Adaptive Support Vector Machine

In locClass: Collection of Local Classification Methods

Description

A local version of Support Vector Machines for classification that puts increased emphasis on a good model fit near the decision boundary.

Usage

  dasvm(x, ...)

  ## S3 method for class 'formula'
 dasvm(formula, data = NULL,
    case.weights = rep(1, nrow(data)), ..., subset,
    na.action = na.omit, scale = TRUE)

  ## Default S3 method:
 dasvm(x, y = NULL,
    wf = c("biweight", "cauchy", "cosine", "epanechnikov", "exponential", "gaussian", "optcosine", "rectangular", "triangular"),
    bw, k, nn.only, itr = 3,
    method = c("prob", "decision"), scale = TRUE,
    type = NULL, case.weights = rep(1, nrow(x)), ...,
    subset = NULL, na.action = na.omit)

Arguments

formula	A symbolic description of the model to be fit.
data	An optional data frame containing the variables in the model. By default the variables are taken from the environment which dasvm is called from.
x	(Required if no formula is given as principal argument.) A data matrix, a vector, or a sparse matrix (object of class Matrix provided by the Matrix package, or of class matrix.csr provided by the SparseM package, or of class simple_triplet_matrix provided by the slam package).
y	(Only if no formula is given as principal argument.) A response vector with one label for each row/component of x. Should be a factor, otherwise it is coerced to a factor with a warning.
wf	A window function which is used to calculate weights that are introduced into the fitting process. Either a character string or a function, e.g. wf = function(x) exp(-x). For details see the documentation for wfs.
bw	(Required only if wf is a string.) The bandwidth parameter of the window function. (See wfs.)
k	(Required only if wf is a string.) The number of nearest neighbors of the decision boundary to be used in the fitting process. (See wfs.)
nn.only	(Required only if wf is a string indicating a window function with infinite support and if k is specified.) Should only the k nearest neighbors or all observations receive positive weights? (See wfs.)
itr	Number of iterations for model fitting, defaults to 3. See also the Details section.
case.weights	Initial observation weights (defaults to a vector of 1s).
method	The method for adaptation to the decision boundary, either "prob" or "decision". Defaults to "prob".
scale	A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions.
type	dasvm can be used only as a classification machine, hence valid options are: C-classification nu-classification C-classification is the default.
...	Further parameters that are passed to wsvm.default, e.g. kernel, degree, gamma, coef0, cost, nu, class.weights, case.weights, cachesize, tolerance, shrinking, cross, probability, fitted, and seed. Note that epsilon is a parameter that is only needed in the regression case and thus will have no effect if specified.
subset	An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
na.action	A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

Details

The idea of Hand and Vinciotti (2003) to put increased weight on observations near the decision boundary is generalized to the multiclass case and applied to Support Vector Machines (SVM).

Two different methods are implemented to achieve this. The first one is based on the decision values. In order to deal with multiclass problems with k classes, k>2, ‘libsvm’ uses the ‘one-against-one’-approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme. Hence, there are decision values for every binary classification problem. The absolute decision values are proportional to the distance between the training observations and the decision boundary. A window function is applied to these distances in order to get observation weights.

The second method is based on posterior probabilities. The probability model for classification fits a logistic distribution using maximum likelihood to the decision values of all binary classifiers, and computes the a-posteriori class probabilities for the multi-class problem using quadratic optimization. The probabilistic regression model assumes (zero-mean) laplace-distributed errors for the predictions, and estimates the scale parameter using maximum likelihood. Observation weights are calculated based on the differences between the two largest estimated posterior probabilities.

Since the decision boundary is not known in advance an iterative procedure is required. First, an unweighted SVM is fitted to the data. Then either based on the estimated decision values or the estimated posterior probabilities observation weights are calculated. Then a weighted SVM (see wsvm) is fitted using these weights. Calculation of weights and model fitting is done several times in turn. The number of iterations is determined by the itr-argument that defaults to 3.

In order to calculate the weights a window function is applied to the decision values or posterior probabilities. The name of the window function (wf) can be specified as a character string. In this case the window function is generated internally in dasvm. Currently supported are "biweight", "cauchy", "cosine", "epanechnikov", "exponential", "gaussian", "optcosine", "rectangular" and "triangular".

Moreover, it is possible to generate the window functions mentioned above in advance (see wfs) and pass them to dasvm.

Any other function implementing a window function can also be used as wf argument. This allows the user to try own window functions. See help on wfs for details.

dasvm calls wsvm internally which is a version of Support Vector Machines that can deal with case weights and which is a modified version of the svm function in package e1071 written by David Meyer (based on C/C++-code by Chih-Chung Chang and Chih-Jen Lin). An extension of LIBSVM that can deal with case weights written by Ming-Wei Chang, Hsuan-Tien Lin, Ming-Hen Tsai, Chia-Hua Ho and Hsiang-Fu Yu is used. It is available at http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances.

libsvm internally uses a sparse data representation, which is also high-level supported by the package SparseM.

If the predictor variables include factors, the formula interface must be used to get a correct model matrix.

Data are scaled internally, usually yielding better results. Parameters of SVM-models usually must be tuned to yield sensible results!

Value

An object of class "dasvm.formula" or "dasvm" inheriting from "wsvm.formula" or "wsvm" and "svm", a list containing the following components:

case.weights	A list of length itr + 1. The initial observation weights (a vector of 1s if none were given) and the observation weights calculated in the individual iterations.
itr	The number of iterations used.
wf	The window function used. Always a function, even if the input was a string.
bw	(Only if wf is a string or was generated by means of one of the functions documented in wfs.) The bandwidth used, NULL if bw was not specified.
k	(Only if wf is a string or was generated by means of one of the functions documented in wfs.) The number of nearest neighbors used, NULL if k was not specified.
nn.only	(Logical. Only if wf is a string or was generated by means of one of the functions documented in wfs and if k was specified.) TRUE if only the k nearest neighbors recieve a positive weight, FALSE otherwise.
adaptive	(Logical.) TRUE if the bandwidth of wf is adaptive to the local density of data points, FALSE if the bandwidth is fixed.
call	The (matched) function call.

References

Hand, D. J., Vinciotti, V. (2003), Local versus global models for classification problems: Fitting models where it matters, The American Statistician, 57(2) 124–130.

See Also

predict.dasvm, wsvm for a weighted version of Support Vector Machines.

Examples

fit <- dasvm(Species ~ ., data = iris, wf = "gaussian", bw = 0.5)
pred <- predict(fit)
mean(pred != iris$Species)

locClass documentation built on May 2, 2019, 5:21 p.m.