Matrix-based incremental local feature selection with dynamic covering granularity

Abstract

Multigranulation rough set is composed of a set of granularities, providing a theoretical framework for solving problems from a multigranulation perspective. Feature selection aims to find the minimal set of attributes that does not compromise the overall classification capability. It has significant applications in the field of information processing. However, in practical application environments, the granularities in information systems often evolve dynamically over time. To address this scenario, an incremental feature selection algorithm for data with changing granularities in local multigranulation neighborhood covering rough sets is proposed. Firstly, the method of local related family is introduced, relationships between matrix operations of local related sets and those of approximate sets are discussed, and feature selection is studied using matrix methods. Subsequently, two matrix-based incremental feature selection algorithms are proposed for the cases where granularity structures in the data are added or deleted due to feature changes. Experiments on six datasets from UCI are then conducted to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the two proposed incremental feature selection algorithms are highly effective.