Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Implementation of Unsupervised Nonlinear Classifier with Binary Harmony Search Algorithm

Binary Harmony Search 알고리즘을 이용한 Unsupervised Nonlinear Classifier 구현

  • Lee, Tae-Ju (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sung, Won-Ki ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 이태주 (중앙대학교 전자전기공학부) ;
  • 박승민 (중앙대학교 전자전기공학부) ;
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 성원기 (강원대학교 전자정보통신공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2013.03.10
  • Accepted : 2013.05.02
  • Published : 2013.08.25
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Abstract

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

본 논문을 통해서 우리는 최적화 알고리즘인 binary harmony search (BHS) 알고리즘을 이용하여 unsupervised nonlinear classifier를 구현하는 방안을 제시하였다. 패턴인식을 위한 기계학습이나 뇌파 신호의 분석 과정과 같이 벡터로 표현되는 특징들을 분류하는데 있어 다양한 알고리즘들이 제시되었다. 교사 학습기반의 분류 방식으로는 support vector machine과 같은 기법이 사용되어왔고, 비교사 학습 방법을 통한 분류 기법으로는 fuzzy c-mean (FCM)과 같은 알고리즘들이 사용되어 왔다. 그러나 기존에 사용해 왔던 분류 방법들은 비선형 데이터 분류에 적용하기 힘들거나 교사 학습을 적용하기 위해서 사전정보를 필요로 하는 문제점이 있다. 본 논문에서는 경험적 접근을 통해 공간상에 분포된 벡터 사이의 기하학적 거리를 최소로 만드는 벡터 집합을 선택하고 이를 하나의 클래스로 간주하는 방법을 적용한 분류법을 제시하였다. 비교 대상으로 FCM과 artificial neural network (ANN) 기반의 self-organizing map (SOM)을 제시하였다. 시뮬레이션에는 KEEL machine learing dataset을 사용하였고 그 결과, 제안된 방식이 기존 알고리즘에 비해 더 나은 우수성을 지니고 있음을 확인하였다.

Keywords

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