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Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with sparse matrix support.
Provided functions
discretize()with additionalequalsizeControl()andmdlControl- discretize a range of numeric attributes in the dataset into nominal attributes. Minimum Description Length (MDL) method is set as the default control. There is also availableequalsizeControl()method.information_gain()- algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute,feature_search()- a convenience wrapper for \code{greedy} and \code{exhaustive} feature selection algorithms that extract valuable attributes depending on the evaluation method (called evaluator),cut_attrs()- select attributes by their score/rank/weights, depending on the cutoff that may be specified by the percentage of the highest ranked attributes or by the number of the highest ranked attributes,to_formula()(misc) - create aformulaobject from a vector.