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Design of Fuzzy Pattern Classifier based on Extreme Learning Machine

Extreme Learning Machine 기반 퍼지 패턴 분류기 설계

  • Ahn, Tae-Chon (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Roh, Sok-Beom (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Hwang, Kuk-Yeon (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Wang, Jihong (Department of Electronics Convergence Engineering, Wonkwang University) ;
  • Kim, Yong Soo (Department of Computer Engineering, Daejeon University)
  • 안태천 (원광대학교 전자 융합공학과) ;
  • 노석범 (원광대학교 전자 융합공학과) ;
  • 황국연 (원광대학교 전자 융합공학과) ;
  • 왕계홍 (원광대학교 전자 융합공학과) ;
  • 김용수 (대전대학교 컴퓨터공학과)
  • Received : 2015.03.22
  • Accepted : 2015.08.04
  • Published : 2015.10.25

Abstract

In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.

본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.

Keywords

References

  1. G. B. Hwang, Q. U. Zhu, C. K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, Vol. 70, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  2. D. Serre, Matrices: Theory and Applications, Springer, New York, 2002.
  3. J. C. Bezdek, R. Ehrlich and W. Full, "FCM: The fuzzy c-means clustering algorithm," Computers & Geoscience, Vol. 10, pp. 191-203, 1984. https://doi.org/10.1016/0098-3004(84)90020-7

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