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Incremental feature learning (IFL) is a supervised learning paradigm for neural networks (NNs), where the input layer is incrementally expanded over time. NNs dynamically expand the input layer with new features, while also reducing overfitting and model complexity. Feature ranking based on feature importance determines the order of feature integration. The incremental nature of IFL results in a dynamic optimization problem (DOP), where both the search space and its dimensionality changes over time. Particle swarm optimisation (PSO) has been extended to dynamic environments. This study adapts various dynamic PSO variants to train incrementally constructed NNs (INNs). The performance of INNs is compared to fully constructed NNs (FNNs) trained with BP and standard PSO on seven regression tasks. Results demonstrate that IFL effectively allows NNs to incorporate new features dynamically and acts as a regularisation technique.

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Incremental Feature Learning of Shallow Feedforward Regression Neural Networks Using Particle Swarm Optimisation

  • 会议论文

  • 发表日期：

    2026年

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  • Ross W. Nayler,
  • Andries P. Engelbrecht

摘要

Incremental feature learning (IFL) is a supervised learning paradigm for neural networks (NNs), where the input layer is incrementally expanded over time. NNs dynamically expand the input layer with new features, while also reducing overfitting and model complexity. Feature ranking based on feature importance determines the order of feature integration. The incremental nature of IFL results in a dynamic optimization problem (DOP), where both the search space and its dimensionality changes over time. Particle swarm optimisation (PSO) has been extended to dynamic environments. This study adapts various dynamic PSO variants to train incrementally constructed NNs (INNs). The performance of INNs is compared to fully constructed NNs (FNNs) trained with BP and standard PSO on seven regression tasks. Results demonstrate that IFL effectively allows NNs to incorporate new features dynamically and acts as a regularisation technique.