Optimization for the Zero-Inflated Binary Classification Model with Regulation Rules Using Evolutionary Algorithms
學年 114
學期 2
出版（發表）日期 2026-05-01
作品名稱 Optimization for the Zero-Inflated Binary Classification Model with Regulation Rules Using Evolutionary Algorithms
作品名稱（其他語言）
著者 Hua Xin; Ya-Yen Fan; Tzong-Ru Tsai
單位
出版者
著錄名稱、卷期、頁數 ICIC Express Letters 20(5), p. 497-503
摘要 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.
關鍵字 Binary classification; Differential evolution algorithm; Particle swarm optimization algorithm; Zero-inflated model; Monte Carlo simulation
語言 en_US
ISSN
期刊性質 國外
收錄於 Scopus
產學合作
通訊作者 T-R Tsai
審稿制度 是
國別 JPN
公開徵稿
出版型式 ,電子版
SDGS 夥伴關係,優質教育