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<p>When designing an inference model, feature selection methods can be used to select a subset of relevant variables from the original set of variables to increase the performance and reduce the complexity of the model. In this work, a new feature selection method exploiting nonlinear relationships between variables is introduced in the context of multi-target regression problems. The proposed approach is based on a neural network with one hidden layer in which a regularization term is introduced to enforce variable selection. Experiments on synthetic and real-world datasets show the effectiveness of the proposed approach.</p>

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Nonlinear Feature Selection for Multi-target Regression Problems

  • 期刊论文

  • 发表日期：

    2025年4月22日

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  • Mamadou Kanouté,
  • Edith Grall-Maës,
  • Pierre Beauseroy

摘要

When designing an inference model, feature selection methods can be used to select a subset of relevant variables from the original set of variables to increase the performance and reduce the complexity of the model. In this work, a new feature selection method exploiting nonlinear relationships between variables is introduced in the context of multi-target regression problems. The proposed approach is based on a neural network with one hidden layer in which a regularization term is introduced to enforce variable selection. Experiments on synthetic and real-world datasets show the effectiveness of the proposed approach.