Zero-inflated binary classification model with elastic net regularization
學年 113
學期 1
出版（發表）日期 2024-09-25
作品名稱 Zero-inflated binary classification model with elastic net regularization
作品名稱（其他語言）
著者 Chen, Hsien-ching; Tsai, Tzong-ru
單位
出版者
著錄名稱、卷期、頁數 Mathematics 12(19),　2990
摘要 Zero inflation and overfitting can reduce the accuracy rate of using machine learning models for characterizing binary data sets. A zero-inflated Bernoulli (ZIBer) model can be the right model to characterize zero-inflated binary data sets. When the ZIBer model is used to characterize zero-inflated binary data sets, overcoming the overfitting problem is still an open question. To improve the overfitting problem for using the ZIBer model, the minus log-likelihood function of the ZIBer model with the elastic net regularization rule for an overfitting penalty is proposed as the loss function. An estimation procedure to minimize the loss function is developed in this study using the gradient descent method (GDM) with the momentum term as the learning rate. The proposed estimation method has two advantages. First, the proposed estimation method can be a general method that simultaneously uses 𝐿1 - and 𝐿2 -norm terms for penalty and includes the ridge and least absolute shrinkage and selection operator methods as special cases. Second, the momentum learning rate can accelerate the convergence of the GDM and enhance the computation efficiency of the proposed estimation procedure. The parameter selection strategy is studied, and the performance of the proposed method is evaluated using Monte Carlo simulations. A diabetes example is used as an illustration.
關鍵字 expectation-maximization algorithm;gradient descent method;learning rate;maximum likelihood estimation;zero-inflated model
語言 en
ISSN 2227-7390
期刊性質 國內
收錄於 SCI
產學合作
通訊作者 Tzong-Ru Tsai
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
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