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  • Simultaneous meta-data and meta-classifier selection in multiple classifier system

    Author(s)
    Nguyen, TT
    Ha, TS
    Luong, AV
    Liew, AWC
    Van Nguyen, TM
    McCall, J
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2019
    Metadata
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    Abstract
    In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method ...
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    In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems.
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    Conference Title
    GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference
    DOI
    https://doi.org/10.1145/3321707.3321770
    Subject
    Neural networks
    Publication URI
    http://hdl.handle.net/10072/391053
    Collection
    • Conference outputs

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