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  • A weighted multiple classifier framework based on random projection

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    Embargoed until: 2021-03-26
    Author(s)
    Tien, Thanh Nguyen
    Manh, Truong Dang
    Liew, Alan WeeChung
    Bezdek, James C
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2019
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    Abstract
    In this paper, we propose a weighted multiple classifier framework based on random projections. Similar to the mechanism of other homogeneous ensemble methods, the base classifiers in our approach are obtained by a learning algorithm on different training sets generated by projecting the original up-space training set to lower dimensional down-spaces. We then apply a Least SquarE−based method to weigh the outputs of the base classifiers so that the contribution of each classifier to the final combined prediction is different. We choose Decision Tree as the learning algorithm in the proposed framework and conduct experiments ...
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    In this paper, we propose a weighted multiple classifier framework based on random projections. Similar to the mechanism of other homogeneous ensemble methods, the base classifiers in our approach are obtained by a learning algorithm on different training sets generated by projecting the original up-space training set to lower dimensional down-spaces. We then apply a Least SquarE−based method to weigh the outputs of the base classifiers so that the contribution of each classifier to the final combined prediction is different. We choose Decision Tree as the learning algorithm in the proposed framework and conduct experiments on a number of real and synthetic datasets. The experimental results indicate that our framework is better than many of the benchmark algorithms, including three homogeneous ensemble methods (Bagging, RotBoost, and Random Subspace), several well-known algorithms (Decision Tree, Random Neural Network, Linear Discriminative Analysis, K Nearest Neighbor, L2-loss Linear Support Vector Machine, and Discriminative Restricted Boltzmann Machine), and random projection-based ensembles with fixed combining rules with regard to both classification error rates and F1 scores.
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    Journal Title
    INFORMATION SCIENCES
    Volume
    490
    DOI
    https://doi.org/10.1016/j.ins.2019.03.067
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Mathematical Sciences
    Information and Computing Sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/384479
    Collection
    • Journal articles

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