Work

A Heuristic Attribute-Reduction Algorithm Based on Conditional Entropy for Incomplete Information Systems

Year: 2024

Type: article

Abstract: With the continuous expansion of databases, the extraction of information has been an urgent research topic in many fields. As an effective method to remove redundant attributes, attribute reduction demonstrates extraordinary ability in simplifying information systems. This paper applies a novel form of conditional entropy to investigate the attribute reduction in incomplete information systems. Firstly, a novel definition of conditional entropy is introduced based on tolerance relation. Additionally, in order to reduce time complexity, we propose a binsearch heuristic attribute-reduction algorithm with conditional entropy as heuristic knowledge. Furthermore, two examples are used to illustrate the feasibility and validity of the reduction algorithm.

Source: Axioms

DOI: 10.3390/axioms13110736

Authors: Yanling Bao, Shumin Cheng

Institution: Xinjiang University

Language: English

Topic: Rough Sets and Fuzzy Logic

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: Natural Science Foundation of Xinjiang Province, Natural Science Foundation of Xinjiang