FSelectorRcpp 0.1.0

Rcpp (free of Java/Weka) implementation of FSelector entropy-based feature selection algorithms with sparse matrix support.

Provided functions

  • discretize() with additional equalsizeControl() and mdlControl - 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 available equalsizeControl() 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 a formula object from a vector.