Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148177
Title: Wrapper-filter feature selection algorithm using a memetic framework
Authors: Zhu, Zexuan
Ong, Yew-Soon
Dash, Manoranjan
Keywords: Engineering::Computer science and engineering
Issue Date: 2007
Source: Zhu, Z., Ong, Y. & Dash, M. (2007). Wrapper-filter feature selection algorithm using a memetic framework. IEEE Transactions On Systems, Man, and Cybernetics, Part B (Cybernetics), 37(1), 70-76. https://dx.doi.org/10.1109/tsmcb.2006.883267
Journal: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Abstract: This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA.
URI: https://hdl.handle.net/10356/148177
ISSN: 1083-4419
DOI: 10.1109/tsmcb.2006.883267
Rights: © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSMCB.2006.883267.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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