Matrix-Based Incremental Feature Selection Method Using Weight-Partitioned Multigranulation Rough Set

Abstract

Incremental feature selection methods have gained increasing research attention as they improve the efficiency of feature selection for dynamic datasets. Multigranulation rough set, as an extension of rough set theory, allows for a comprehensive and rational analysis of problems from multiple hierarchical and granular perspectives.However, existing research on granularity partitioning relies on the decision maker's subjective experience, which lacks convincing power. In this paper, we propose a generalized multigranulation neighborhood rough set based on weight partition model, using a matrix form. We discuss several properties and define a new entropy measure to evaluate feature importance. A heuristic feature selection algorithm is developed based on this entropy to search for the optimal subset. Furthermore, we discuss dynamic updating mechanism and design two incremental feature selection algorithms. Finally, we conduct experiments on 12 public datasets to evaluate the performance of the proposed algorithms and validate their effectiveness and efficiency in feature selection for both static and dynamic datasets.