Local Indiscernibility Relation Reduction for Information Tables

Abstract

Attribute reduction comes from machine learning and is an important component of rough set theory. Research on attribute reduction has produced many important achievements. The aim of attribute reduction is to reduce the complexity of data while retaining its original characteristics to the greatest extent. The concept of attribute reduction is of great significance in machine learning research. In previous studies, a variety of attribute reduction definitions have been proposed according to different rules. Based on the binary relations among objects and local decision rules, this paper describes a local indiscernibility relation reduction for information tables. The discernibility matrix for the proposed reduction is established, and examples for single- and multi-decision classes are presented to illustrate that the proposed local indiscernibility relation reduction can be applied to decision tables. According to the reduction concept developed in this paper, and considering a heuristic algorithm for calculating the significance of attributes and a binary integer programming algorithm based on the discernibility matrix, three reduction algorithms are proposed. Experiments are conducted using four classifiers and a number of publicly available datasets. A comparison of the experimental results presented in this paper demonstrates the feasibility of the proposed algorithms.