Description Usage Arguments Details Value References See Also Examples
Information-theoretic meta-features are particularly appropriate to describe discrete (categorical) attributes, but they also fit continuous ones so a discretization is required.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
... |
Further arguments passed to the summarization functions. |
x |
A data.frame contained only the input attributes. |
y |
A factor response vector with one label for each row/component of x. |
features |
A list of features names or |
summary |
A list of summarization functions or empty for all values. See
post.processing method to more information. (Default:
|
transform |
A logical value indicating if the numeric attributes should
be transformed. If |
formula |
A formula to define the class column. |
data |
A data.frame dataset contained the input attributes and class The details section describes the valid values for this group. |
The following features are allowed for this method:
Attributes concentration. It is the Goodman and Kruskal's tau measure otherwise known as the concentration coefficient computed for each pair of attributes (multi-valued).
Attributes entropy, a measure of randomness of each attributes in the dataset (multi-valued).
Class concentration, similar to "attrConc", however, it is computed for each attribute and the class (multi-valued).
Class entropy, which describes how much information is necessary to specify the class in the dataset.
Equivalent number of attributes, which represents the number of attributes suitable to optimally solve the classification task using the dataset.
Joint entropy, which represents the total entropy of each attribute and the class (multi-valued).
Mutual information, that is the common information shared between each attribute and the class in the dataset (multi-valued).
Noise ratio, which describes the amount of irrelevant information contained in the dataset.
This method uses the unsupervised data discretization procedure provided by
discretize function, where the default values are used when
transform=TRUE
.
A list named by the requested meta-features.
Donald Michie, David J. Spiegelhalter, Charles C. Taylor, and John Campbell. Machine Learning, Neural and Statistical Classification, volume 37. Ellis Horwood Upper Saddle River, 1994.
Alexandros Kalousis and Melanie Hilario. Model selection via meta-learning: a comparative study. International Journal on Artificial Intelligence Tools, volume 10, pages 525 - 554, 2001.
Ciro Castiello, Giovanna Castellano, and Anna Maria Fanelli. Meta-data: Characterization of input features for meta-learning. In 2nd International Conference on Modeling Decisions for Artificial Intelligence (MDAI), pages 457 - 468, 2005.
Other meta-features:
clustering()
,
complexity()
,
concept()
,
general()
,
itemset()
,
landmarking()
,
model.based()
,
relative()
,
statistical()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Extract all metafeatures
infotheo(Species ~ ., iris)
## Extract some metafeatures
infotheo(iris[1:4], iris[5], c("classEnt", "jointEnt"))
## Extract all meta-features without summarize the results
infotheo(Species ~ ., iris, summary=c())
## Use another summarization functions
infotheo(Species ~ ., iris, summary=c("min", "median", "max"))
## Do not transform the data (using only categorical attributes)
infotheo(Species ~ ., iris, transform=FALSE)
|
$attrConc
mean sd
0.2098049 0.1195880
$attrEnt
mean sd
2.27719128 0.06103943
$classConc
mean sd
0.2734739 0.1409110
$classEnt
[1] 1.584963
$eqNumAttr
[1] 1.878064
$jointEnt
mean sd
3.0182196 0.3821883
$mutInf
mean sd
0.8439342 0.4222026
$nsRatio
[1] 1.698304
$classEnt
[1] 1.584963
$jointEnt
mean sd
3.0182196 0.3821883
$attrConc
non.aggregated1 non.aggregated2 non.aggregated3 non.aggregated4
0.08478340 0.26374940 0.23291127 0.09183055
non.aggregated5 non.aggregated6 non.aggregated7 non.aggregated8
0.11612406 0.12836408 0.25689542 0.12161444
non.aggregated9 non.aggregated10 non.aggregated11 non.aggregated12
0.42995680 0.23009982 0.13924810 0.42208100
$attrEnt
non.aggregated.Sepal.Length non.aggregated.Sepal.Width
2.315653 2.186232
non.aggregated.Petal.Length non.aggregated.Petal.Width
2.308260 2.298620
$classConc
non.aggregated.Sepal.Length non.aggregated.Sepal.Width
0.1882864 0.1197396
non.aggregated.Petal.Length non.aggregated.Petal.Width
0.3847270 0.4011426
$classEnt
[1] 1.584963
$eqNumAttr
[1] 1.878064
$jointEnt
non.aggregated.Sepal.Length non.aggregated.Sepal.Width
3.281389 3.410577
non.aggregated.Petal.Length non.aggregated.Petal.Width
2.698910 2.682002
$mutInf
non.aggregated.Sepal.Length non.aggregated.Sepal.Width
0.6192261 0.3606172
non.aggregated.Petal.Length non.aggregated.Petal.Width
1.1943125 1.2015809
$nsRatio
[1] 1.698304
$attrConc
min median max
0.0847834 0.1846740 0.4299568
$attrEnt
min median max
2.186232 2.303440 2.315653
$classConc
min median max
0.1197396 0.2865067 0.4011426
$classEnt
[1] 1.584963
$eqNumAttr
[1] 1.878064
$jointEnt
min median max
2.682002 2.990150 3.410577
$mutInf
min median max
0.3606172 0.9067693 1.2015809
$nsRatio
[1] 1.698304
$attrConc
mean sd
NA NA
$attrEnt
mean sd
NA NA
$classConc
mean sd
NA NA
$classEnt
[1] NA
$eqNumAttr
[1] NA
$jointEnt
mean sd
NA NA
$mutInf
mean sd
NA NA
$nsRatio
[1] NA
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