Attribute reduction and rule acquisition of formal decision context based on two new kinds of decision rules

TL;DR

This paper introduces new types of decision rules for formal decision contexts, proposes algorithms for rule acquisition and attribute reduction, and demonstrates their effectiveness through comparative analysis.

Contribution

It presents two novel decision rules and associated algorithms for rule acquisition and attribute reduction in formal decision contexts.

Findings

Algorithms perform well compared to existing methods

Attribute reduction preserves decision rules effectively

New decision rules enhance formal decision context analysis

Abstract

This paper mainly studies the rule acquisition and attribute reduction for formal decision context based on two new kinds of decision rules, namely I-decision rules and II-decision rules. The premises of these rules are object-oriented concepts, and the conclusions are formal concept and property-oriented concept respectively. The rule acquisition algorithms for I-decision rules and II-decision rules are presented. Some comparative analysis of these algorithms with the existing algorithms are examined which shows that the algorithms presented in this study behave well. The attribute reduction approaches to preserve I-decision rules and II-decision rules are presented by using discernibility matrix.