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  • A Novel Genetic Algorithm Approach for Simultaneous Feature and Classifier Selection in Multi Classifier System

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    Author(s)
    Tien, Thanh Nguyen
    Liew, Alan Wee-Chung
    Xuan, Cuong Pham
    Minh, Toan Tran
    Mai, Phuong Nguyen
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2014
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    Abstract
    In this paper we introduce a novel approach for classifier and feature selection in a multi-classifier system using Genetic Algorithm (GA). Specifically, we propose a 2-part structure for each chromosome in which the first part is encoding for classifier and the second part is encoding for feature. Our structure is simple in the implementation of the crossover as well as the mutation stage of GA. We also study 8 different fitness functions for our GA based algorithm to explore the optimal fitness functions for our model. Experiments are conducted on both 14 UCI Machine Learning Repository and CLEF2009 medical image database ...
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    In this paper we introduce a novel approach for classifier and feature selection in a multi-classifier system using Genetic Algorithm (GA). Specifically, we propose a 2-part structure for each chromosome in which the first part is encoding for classifier and the second part is encoding for feature. Our structure is simple in the implementation of the crossover as well as the mutation stage of GA. We also study 8 different fitness functions for our GA based algorithm to explore the optimal fitness functions for our model. Experiments are conducted on both 14 UCI Machine Learning Repository and CLEF2009 medical image database to demonstrate the benefit of our model on reducing classification error rate.
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    Conference Title
    2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
    Publisher URI
    http://www.ieee-wcci2014.org/index.htm
    DOI
    https://doi.org/10.1109/CEC.2014.6900377
    Copyright Statement
    © 2014 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.
    Subject
    Neural, Evolutionary and Fuzzy Computation
    Publication URI
    http://hdl.handle.net/10072/66707
    Collection
    • Conference outputs

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