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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Li, R., & Huang, D. (2005). Reducts in incomplete decision tables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3584 LNAI, pp. 165–174). Springer Verlag. https://doi.org/10.1007/11527503_20
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