A Hybrid Algorithm with a Data Augmentation Method to Enhance the Performance of the Zero-Inflated Bernoulli Model

Chih-Jen Su, I-Fei Chen, Tzong-Ru Tsai () and Yuhlong Lio
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Chih-Jen Su: Department of Management Sciences, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
I-Fei Chen: Department of Management Sciences, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
Tzong-Ru Tsai: Department of Statistics, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan
Yuhlong Lio: Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: 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.

Keywords: data augmentation; gradient descent method; Monte Carlo simulation; particle swarm optimization; SMOTE (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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