Learning Style Classification with Weighted Distance Grey Wolf Optimization

説明

This research aims to improve the performance of multiclass classification by using grey wolf optimization algorithm. The proposed algorithm presents a solution to improve the grey wolf optimization performance using weighted distance and immigration operation. The weight distance is used for the omega wolves movement is defined from fitness value of each leader. The proposed technique is based on learning style prediction which addresses multiclass classification problem. The results showed that the proposed technique obtained the higher accuracy rate than other classification techniques.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390282763100907264
  • DOI
    10.5954/icarob.2019.gs3-2
  • ISSN
    21887829
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • Crossref
  • 抄録ライセンスフラグ
    使用不可

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