Binary Classification with Imbalanced Data

淡江大學機構典藏 > 商管學院 > 風險管理與保險學系 > 期刊論文 > Item 987654321/124910

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题名:	Binary Classification with Imbalanced Data
作者:	Ho, Chia-ling
关键词:	artificial neural network;expectation-maximization algorithm;Entropy;logistic regression;zero-inflated model
日期:	2023-12-22
上传时间:	2024-01-10 12:05:15 (UTC+8)
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.
關聯:	Entropy 2024 26(1),　15
DOI:	10.3390/e26010015
显示于类别:	[風險管理與保險學系] 期刊論文

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