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Feature-wise differential evolution classifier with an OWA-based multi-distance aggregation

Author

Listed:
  • David Koloseni
  • Mario Fedrizzi
  • Pasi Luukka
  • Mikael Collan
  • Jouni Lampinen

Abstract

This paper describes a new classification method that uses the differential evolution algorithm to find an optimal parameter selection for classification of objects. Parameters optimised include the distance measure used, distance parameters, class vectors for each class, and OWA-parameters. The distances yielded for each feature by the optimised distance measures are aggregated into an overall distance by using OWA-based multi-distance aggregation. In this paper, OWA-based multi-distances are applied for the first time in a classification problem. The OWA-based multi-distance aggregation method is tested with five possible OWA-based aggregation schemes and for each one of them an optimal value is computed with DE. The method is tested with five datasets and results show improvement in classification accuracy compared to previous literature.

Suggested Citation

  • David Koloseni & Mario Fedrizzi & Pasi Luukka & Mikael Collan & Jouni Lampinen, 2016. "Feature-wise differential evolution classifier with an OWA-based multi-distance aggregation," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 9(4), pages 436-451.
  • Handle: RePEc:ids:ijmore:v:9:y:2016:i:4:p:436-451
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