Neural partially linear additive model

文献类型： 外文期刊

第一作者： Zhu, Liangxuan

作者： Zhu, Liangxuan;Li, Han;Zhang, Xuelin;Wu, Lingjuan;Chen, Hong;Chen, Hong;Chen, Hong;Chen, Hong

作者机构：

关键词： feature selection; structure discovery; partially linear additive model; neural network

期刊名称：FRONTIERS OF COMPUTER SCIENCE （ 影响因子：4.2； 五年影响因子：3.0 ）

ISSN： 2095-2228

年卷期： 2024 年 18 卷 6 期

页码：

收录情况： SCI

摘要： Interpretability has drawn increasing attention in machine learning. Most works focus on post-hoc explanations rather than building a self-explaining model. So, we propose a Neural Partially Linear Additive Model (NPLAM), which automatically distinguishes insignificant, linear, and nonlinear features in neural networks. On the one hand, neural network construction fits data better than spline function under the same parameter amount; on the other hand, learnable gate design and sparsity regular-term maintain the ability of feature selection and structure discovery. We theoretically establish the generalization error bounds of the proposed method with Rademacher complexity. Experiments based on both simulations and real-world datasets verify its good performance and interpretability.

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