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Classification of attention levels using a Random Forest algorithm optimized with Particle Swarm Optimization

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Abstract

Attention is one of the most important cognitive functions since it allows us to discriminate irrelevant stimuli when performing an activity. The presence of an attention deficit significantly affects a person’s performance. This is one of the reasons why it is of utmost importance to determine the state of attention mechanisms.A tool that allows determining the level of attention could be of great help in the diagnosis of syndromes or disorders, as well as in the rehabilitation and treatment of people suffering from attention deficits. In this work, a methodology is proposed based on a Random Forest algorithm optimized with PSO (Particle Swarm Optimization) for the classification of attention levels. These attention levels are divided into three main categories: High Attention, Normal Attention, and Low Attention. The proposed approach demonstrated reaching an accuracy of up to 96%. Finally, the approach from this contribution was compared with the state of the art, demonstrating that this work is a feasible methodology for this application.

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Authors and Affiliations

  1. Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro, Mexico

    María Guadalupe Bedolla-Ibarra, Maria del Carmen Cabrera-Hernandez, Marco Antonio Aceves-Fernández & Saul Tovar-Arriaga

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  1. María Guadalupe Bedolla-Ibarra
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  2. Maria del Carmen Cabrera-Hernandez
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  3. Marco Antonio Aceves-Fernández
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  4. Saul Tovar-Arriaga
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Correspondence to Marco Antonio Aceves-Fernández.

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Bedolla-Ibarra, M.G., Cabrera-Hernandez, M.d.C., Aceves-Fernández, M.A. et al. Classification of attention levels using a Random Forest algorithm optimized with Particle Swarm Optimization. Evolving Systems 13, 687–702 (2022). https://doi.org/10.1007/s12530-022-09444-2

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