| Summary: | Feature selection is a process of choosing a subset of relevant features so that the quality of predictive models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual Information (MI) between subsets of features and class labels. The current methods use a lower order approximation, by treating the joint entropy as a summation of several single variable entropies. This leads to locally optimal selections and misses correlated (multi-way) non-local feature combinations. In this article we present a higher order MI-based approximation technique called Higher Order Feature Selection (HOFS) implemented in R software. Instead of producing a single list of features, our method produces a ranked collection of feature subsets that maximizes MI, giving better comprehension (feature ranking) as to which features work best together when selected, due to their underlying interdependence. We demonstrate that the proposed method performs better than existing feature selection approaches while keeping similar running times and computational complexity.
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