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The primary goal of feature selection is to identify and retain a subset of relevant and informative features from the original dataset, thereby reducing data dimensionality and enhancing the overall performance of the model. Bioinspired approaches, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are particularly well suited for handling the complexity of feature selection for multimodal data, discovering multiple optimal solutions, and managing complex feature interactions. This paper explores the capabilities of these techniques by means of a software agent capable of performing multimodal feature mining for a synthetically generated multimodal dataset.

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Feature Selection with Evolutionary and Swarm Algorithms for Multimodal Learning on a Synthetic Dataset

  • 会议论文

  • 发表日期：

    2026年

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  • Rafael Marin Machado de Souza,
  • Lucas Vinicius Buchelt Souza,
  • Deive Leal,
  • Marcio Biczyk,
  • Marcelo Amorim,
  • Leandro de Castro

摘要

The primary goal of feature selection is to identify and retain a subset of relevant and informative features from the original dataset, thereby reducing data dimensionality and enhancing the overall performance of the model. Bioinspired approaches, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are particularly well suited for handling the complexity of feature selection for multimodal data, discovering multiple optimal solutions, and managing complex feature interactions. This paper explores the capabilities of these techniques by means of a software agent capable of performing multimodal feature mining for a synthetically generated multimodal dataset.