出版日期：2025-05-22 00:00:00

著者：Chih-Jen Su; I-Fei Chen; Tzong-Ru Tsai; Yuhlong Lio

著錄名稱、卷期、頁數：Mathematics 13(11), p.1702

摘要：The zero-inflated Bernoulli model, enhanced with elastic net regularization, effectively handles binary classification for zero-inflated datasets. This zero-inflated structure significantly contributes to data imbalance. To improve the ZIBer model’s ability to accurately identify minority classes, we explore the use of momentum and Nesterov’s gradient descent methods, particle swarm optimization, and a novel hybrid algorithm combining particle swarm optimization with Nesterov’s accelerated gradient techniques. Additionally, the synthesized minority oversampling technique is employed for data augmentation and training the model. Extensive simulations using holdout cross-validation reveal that the proposed hybrid algorithm with data augmentation excels in identifying true positive cases. Conversely, the hybrid algorithm without data augmentation is preferable when aiming for a balance between the metrics of recall and precision. Two case studies about diabetes and biopsy are provided to demonstrate the model’s effectiveness, with performance assessed through K-fold cross-validation.

關鍵字：data augmentation; gradient descent method; Monte Carlo simulation; particle swarm optimization; SMOTE

語言：zh_TW

ISSN：2227-7390

期刊性質：國內

收錄於：SCI Scopus

通訊作者：Tzong-Ru Tsai

審稿制度：否

國別：CHE

出版型式：電子版