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Ming Zheng

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EAAI Journal 2024 Journal Article

Exploratory parallel hybrid sampling framework for imbalanced data classification

  • Ming Zheng
  • Zhuo Zhao
  • Fei Wang
  • Xiaowen Hu
  • Sheng Xu
  • Wanggen Li
  • Tong Li

Current engineering application scenarios often face the challenge of imbalanced data, hybrid sampling is an effective method to deal with the imbalanced data classification issue, which can avoid the issues of overfitting and mistakenly deleting useful majority samples when using oversampling approach and undersampling approach alone. However, at present most of the hybrid sampling approaches are implemented serially, and the implementation of oversampling and undersampling approaches alone will cause mutual interference and influence between them. This study proposes a parallel hybrid sampling framework based on the idea of parallel engineering and theoretically analyzes its superiority. The experimental results show that when applied to five classification algorithms with three performance evaluation metrics, the proposed framework outperforms the two mainstream hybrid sampling frameworks. Moreover, the proposed framework can effectively reduce the time consumption of hybrid sampling process.

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NeurIPS Conference 2023 Conference Paper

Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions

  • Ruofan Wu
  • Jiawei Qiao
  • Mingzhe Wu
  • Wen Yu
  • Ming Zheng
  • Tengfei Liu
  • Tianyi Zhang
  • Weiqiang Wang

We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models achieve predictive performance comparable to or sometimes surpassing state-of-the-art survival models. Our code is publicly availabel at https: //github. com/Rorschach1989/nfm

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