文章基本信息

标题：Metric Based Attribute Reduction Method in Dynamic Decision Tables
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作者：Demetrovics Janos ; Nguyen Thi Lan Huong ; Vu Duc Thi 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2016
卷号：16
期号：2
页码：3
DOI：10.1515/cait-2016-0016
出版社：Bulgarian Academy of Science
摘要：Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough set theory is also called attribute reduction. It has attracted a lot of attention from researchers and numerous potential results have been gained. However, most of them are applied on static data and attribute reduction in dynamic databases is still in its early stages. This paper focuses on developing incremental methods and algorithms to derive reducts, employing a distance measure when decision systems vary in condition attribute set. We also conduct experiments on UCI data sets and the experimental results show that the proposed algorithms are better in terms of time consumption and reducts' cardinality in comparison with non-incremental heuristic algorithm and the incremental approach using information entropy proposed by authors in [17].
关键词：Rough set; decision systems; attribute reduction; reduct; metric.