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  • Evolving interval-based representation for multiple classifier fusion

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    Embargoed until: 2022-05-16
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    Accepted Manuscript (AM)
    Author(s)
    Tien, Thanh Nguyen
    Manh, Truong Dang
    Baghel, Vimal Anand
    Anh, Vu Luong
    McCall, John
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Luong, Vu A.
    Year published
    2020
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    Abstract
    Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the ...
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    Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO.
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    Journal Title
    Knowledge-Based Systems
    Volume
    201
    DOI
    https://doi.org/10.1016/j.knosys.2020.106034
    Copyright Statement
    © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Information and Computing Sciences
    Commerce, Management, Tourism and Services
    Psychology and Cognitive Sciences
    Science & Technology
    Computer Science, Artificial Intelligence
    Ensemble method
    Publication URI
    http://hdl.handle.net/10072/397718
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    • Journal articles

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