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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/129172


    Title: Optimization for the Zero-Inflated Binary Classification Model with Regulation Rules Using Evolutionary Algorithms
    Authors: Xin, Hua;Fan, Ya-Yen;Tsai, Tzong-Ru
    Keywords: Binary classification;Differential evolution algorithm;Particle swarm optimization algorithm;Zero-inflated model;Monte Carlo simulation
    Date: 2026-05
    Issue Date: 2026-04-09 12:05:18 (UTC+8)
    Abstract: To improve the classification quality of using the regulation rule in a zeroinflated binary (ZIB) model, the differential evolution (DE) and particle swarm optimization (PSO) algorithms are used in this study for optimization. The performance of the
    two algorithms is compared with the maximum likelihood estimation method. The elastic
    net regularization rule (ENR) is used to construct the loss function for the ZIB model,
    named the ENR-ZIB model, to prevent overfitting. The estimates of the model parameters
    of the ENR-ZIB model are obtained to minimize a specified loss function. Moreover, the
    classification performance of the obtained model is studied. Monte Carlo simulations are
    conducted to compare the performance of the ENR-ZIB model using two proposed optimization procedures with the maximum likelihood estimation method. Simulation results
    show that the proposed optimization procedures can have a good classification quality for
    the ENR-ZIB model.
    Relation: ICIC Express Letters 20(5), p. 497-503
    DOI: 10.24507/icicel.20.05.497
    Appears in Collections:[統計學系暨研究所] 期刊論文

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