Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5319
Title: Fuzzy modified great deluge algorithm for attribute reduction
Authors: Mafarja, Majdi
Abdullah, Salwani
Keywords: Fuzzy logic
Problem solving
Rough sets
Fuzzy algorithms
Data mining
Feature selection
Great deluge algorithm
Issue Date: Jan-2014
Publisher: Springer
Citation: Mafarja 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, Cham
Abstract: This 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.
URI: http://hdl.handle.net/20.500.11889/5319
Appears in Collections:Fulltext Publications

Files in This Item:
File Description SizeFormat 
02 Fuzzy m-GD-SCDM-2013_Paper-34.pdf471.33 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.