Information Theoretic Multi-Target Feature Selection via Output Space Quantization

Information Theoretic Feature Selection and Projection

Information-theoretic algorithm for feature selection

Feature selection is used to improve efficiency of learning algorithms by finding an optimal subset of features. However, most feature selection techniques can handle only certain types of data. Additional limitations of existing methods include intensive computational requirements and inability to identify redundant variables. In this paper, we are presenting a novel, information-theoretic alg...

Information Theoretic Feature Selection for Clustering

Information-theoretic feature selection for functional data classification

The classification of functional or high-dimensional data requires to select a reduced subset of features among the initial set, both to help fighting the curse of dimensionality and to help interpreting the problem and the model. The mutual information criterion may be used in that context, but it suffers from the difficulty of its estimation through a finite set of samples. Efficient estimato...

A New Perspective for Information Theoretic Feature Selection

Feature Filters are among the simplest and fastest approaches to feature selection. A filter defines a statistical criterion, used to rank features on how useful they are expected to be for classification. The highest ranking features are retained, and the lowest ranking can be discarded. A common approach is to use the Mutual Information between the feature and class label. This area has seen ...