Reducts in incomplete decision tables

Authors:
Renpu Li;Dao Huang
Affiliations:
College of Computer Science and Technology, Yantai Normal University, Yantai, China;College of Information, East China University of Science and Technology, Shanghai, China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

Citing 5
Cited 1

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Reducts and Constructs in Attribute Reduction

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Feature selection using rough entropy-based uncertainty measures in incomplete decision systems

Knowledge-Based Systems

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Abstract

Knowledge reduction is an important issue in data mining. This paper focuses on the problem of knowledge reduction in incomplete decision tables. Based on a concept of incomplete conditional entropy, a new reduct definition is presented for incomplete decision tables and its properties are analyzed. Compared with several existing reduct definitions, the new definition has a better explanation for knowledge uncertainty and is more convenient for application of the idea of approximate reduct in incomplete decision tables.