Multi-class contour preserving classification

  • Authors:

  • Piyabute Fuangkhon;Thitipong Tanprasert

  • Affiliations:

  • Distributed and Parallel Computing Research Laboratory, Faculty of Science and Technology, Assumption University, Bangkok, Thailand;Distributed and Parallel Computing Research Laboratory, Faculty of Science and Technology, Assumption University, Bangkok, Thailand

  • Venue:
  • IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
  • Year:
  • 2012

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Abstract

The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neural network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classification. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class datasets from UCI machine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.