Ensemble selector for attribute reduction

Abstract

Through abstracting commonness from the existing heuristic algorithms, control strategies bring us higher level understandings of building reducts in rough set theory. To further improve the performances and strengthen the applicabilities of the addition control strategy, an ensemble selector is introduced into such framework. This ensemble selector is realized through using a set of the fitness functions which may be constructed by homogenous or heterogeneous evaluations of the candidate attributes. Based on the neighborhood rough set model, the experimental results tell us that by comparing the traditional addition control strategy, ensemble selector is effective in improving the stabilities of the reducts, the stabilities of the classification results and the AUC values from the viewpoints of KNN and SVM classifiers. This study suggests new trends for considering attribute reduction problems and provides guidelines for designing new algorithms in rough set theory.