Binary Classification with Imbalanced Data
學年 112
學期 1
出版(發表)日期 2023-12-22
作品名稱 Binary Classification with Imbalanced Data
著者 Jyun-You Chiang, Yuhlong Lio, Chien-Ya Hsu, Chia-Ling Ho, Tzong-Ru Tsai
著錄名稱、卷期、頁數 Entropy 2024 26(1), 15
摘要 When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm is proposed to derive the maximum likelihood estimates of the model parameters. The logistic regression model links the Bernoulli probabilities with the covariates in the ZIBer model, and the prediction performance among the ZIBer model, LightGBM, and artificial neural network (ANN) procedures is compared by Monte Carlo simulation. The results show that no method can dominate the other methods regarding predictive performance under the imbalanced data. The LightGBM and ZIBer models are more competitive than the ANN model for zero-inflated-imbalanced data sets.
關鍵字 artificial neural network;expectation-maximization algorithm;Entropy;logistic regression;zero-inflated model
語言 en
ISSN 1099-4300
期刊性質 國外
收錄於 SCI Scopus
產學合作 國外
國別 CHE
出版型式 ,電子版

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