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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2013

01.08.2013 | Original Article

Consistency-preserving attribute reduction in fuzzy rough set framework

verfasst von: Yuhua Qian, Jiye Liang, Wei Wei

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2013

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Abstract

Attribute reduction (feature selection) has become an important challenge in areas of pattern recognition, machine learning, data mining and knowledge discovery. Based on attribute reduction, one can extract fuzzy decision rules from a fuzzy decision table. As consistency is one of several criteria for evaluating the decision performance of a decision-rule set, in this paper, we devote to present a consistency-preserving attribute reduction in fuzzy rough set framework. Through constructing the membership function of an object, we first introduce a consistency measure to assess the consistencies of a fuzzy target set and a fuzzy decision table, which underlies a foundation for attribute reduction algorithm. Then, we derive two attribute significance measures based on the proposed consistency measure and design a forward greedy algorithm (ARBC) for attribute reduction from both numerical and nominal data sets. Numerical experiments show the validity of the proposed algorithm from search strategy and heuristic function in the meaning of consistency. Number of the selected features is the least for a given threshold of consistency measure.
Vorheriger Artikel Investigation of relationship between object-oriented metrics and change proneness
Nächster Artikel Performance of global–local hybrid ensemble versus boosting and bagging ensembles

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Metadaten
Titel
Consistency-preserving attribute reduction in fuzzy rough set framework
verfasst von
Yuhua Qian
Jiye Liang
Wei Wei
Publikationsdatum
01.08.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2013
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0090-z

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