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Neural partially linear additive model |
Liangxuan ZHU1, Han LI1( ), Xuelin ZHANG1, Lingjuan WU1, Hong CHEN1,2,3,4 |
1. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 2. Engineering Research Center of Intelligent Technology for Agriculture (Ministry of Education), Huazhong Agricultural University, Wuhan 430070, China 3. Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China 4. Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China |
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Abstract 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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Keywords
feature selection
structure discovery
partially linear additive model
neural network
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Corresponding Author(s):
Han LI
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Just Accepted Date: 04 July 2023
Issue Date: 07 October 2023
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