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

Autor:	Yanling Bao, Shumin Cheng
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	attribute reduction conditional entropy binsearch heuristic algorithm tolerance relation incomplete information system Mathematics QA1-939
Zdroj:	Axioms, Vol 13, Iss 11, p 736 (2024)
Druh dokumentu:	article
ISSN:	2075-1680 96761008
DOI:	10.3390/axioms13110736
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/967610083f144e90a479b25d2d1ab02a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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