Heuristic Solutions for the (A, B)-K Feature Set Problem

Abstract

The (a,b)-k Feature Set Problem (FSP) is a combinatorial optimization-based approach for selecting features. The (a,b)-k FSP selects a set of features such that the set maximizes the similarities between entities of the same group and the differences between entities of different groups. This study develops two heuristic algorithms for the (a,b)-k FSP. We tested the algorithms on 11 real-world instances ranging from medium to large. The computational results demonstrate the proposed heuristics compete well against the standard solver CPLEX.

Keywords: Heuristics, Feature Selection, Combinatorial Optimization, Data Analysis