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[Research on eye movement data classification using support vector machine with improved whale optimization

Yinhong Shen1, Chang Zhang1, Lin Yang1

  • 1College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved whale algorithm to optimize support vector machines for enhanced eye movement pattern classification, significantly improving accuracy in autism diagnosis.

Keywords:
Eye movement data classificationHybrid improvement.Support vector machineWhale optimization algorithm

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Area of Science:

  • Computational Neuroscience
  • Machine Learning

Context:

  • Support vector machines (SVMs) are sensitive to parameter selection for eye movement pattern classification.
  • Accurate classification of eye movement data is crucial for various applications, including medical diagnosis.

Purpose:

  • To propose an improved whale algorithm for optimizing SVM parameters in eye movement data classification.
  • To enhance the accuracy and efficiency of eye movement pattern recognition.

Summary:

  • The study extracts fixation and saccade features, applying ReliefF for selection.
  • An improved whale algorithm with inertia weights and differential variation optimizes SVMs, addressing convergence and local minima issues.
  • Experiments on test functions demonstrate superior convergence accuracy and speed.

Impact:

  • The optimized SVM model shows significantly improved accuracy in classifying eye movement data for autism compared to traditional methods.
  • This approach offers a novel method for eye movement pattern recognition and holds potential for assisting medical diagnosis with eye-tracking technology.