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dc.contributor.authorMafarja, Majdi
dc.contributor.authorAbdullah, Salwani
dc.identifier.citationMafarja M., Abdullah S. (2014) Fuzzy Modified Great Deluge Algorithm for Attribute Reduction. In: Herawan T., Ghazali R., Deris M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Chamen_US
dc.description.abstractThis paper proposes a local search meta-heuristic free of parameter tuning to solve the attribute reduction problem. Attribute reduction can be defined as the process of finding minimal subset of attributes from an original set with minimum loss of information. Rough set theory has been used for attribute reduction with much success. However, the reduction method inside rough set theory is applicable only to small datasets, since finding all possible reducts is a time consuming process. This motivates many researchers to find alternative approaches to solve the attribute reduction problem. The proposed method, Fuzzy Modified Great Deluge algorithm (Fuzzy-mGD), has one generic parameter which is controlled throughout the search process by using a fuzzy logic controller. Computational experiments confirmed that the Fuzzy-mGD algorithm produces good results, with greater efficiency for attribute reduction, when compared with other meta-heuristic approaches from the literature.en_US
dc.subjectFuzzy logicen_US
dc.subjectProblem solvingen_US
dc.subjectRough setsen_US
dc.subjectFuzzy algorithmsen_US
dc.subjectData miningen_US
dc.subjectSeparation of variables
dc.subjectGreat deluge algorithm
dc.titleFuzzy modified great deluge algorithm for attribute reductionen_US
dc.typeBook chapteren_US
newfileds.departmentEngineering and Technologyen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
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