Metric Based Attribute Reduction Method in Dynamic Decision Tables
| Autor: | Nguyen Thi Lan Huong, Vu Duc Thi, Demetrovics Janos, Nguyen Long Giang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
business.industry Computer science Feature selection 02 engineering and technology computer.software_genre Machine learning Set (abstract data type) Cardinality Knowledge extraction 020204 information systems Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Attribute domain Rough set Artificial intelligence Data mining business Decision table computer |
| Zdroj: | Cybernetics and Information Technologies. 16:3-15 |
| ISSN: | 1314-4081 |
| Popis: | 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]. |
| Databáze: | OpenAIRE |
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