Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Classification of Parkinson's Disease Using Defuzzification-Based Instance Selection

역퍼지화 기반의 인스턴스 선택을 이용한 파킨슨병 분류

  • Lee, Sang-Hong (Department of Computer Science & Engineering, Anyang University)
  • Received : 2014.03.14
  • Accepted : 2014.04.16
  • Published : 2014.06.30
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Abstract

This study proposed new instance selection using neural network with weighted fuzzy membership functions(NEWFM) based on Takagi-Sugeno(T-S) fuzzy model to improve the classification performance. The proposed instance selection adopted weighted average defuzzification of the T-S fuzzy model and an interval selection, same as the confidence interval in a normal distribution used in statistics. In order to evaluate the classification performance of the proposed instance selection, the results were compared with depending on whether to use instance selection from the case study. The classification performances of depending on whether to use instance selection show 77.33% and 78.19%, respectively. Also, to show the difference between the classification performance of depending on whether to use instance selection, a statistics methodology, McNemar test, was used. The test results showed that the instance selection was superior to no instance selection as the significance level was lower than 0.05.

본 논문에서는 분류 성능을 향상하기 위해서 Takagi-Sugeno(T-S) 퍼지 모델 기반의 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions; NEWFM)을 이용한 새로운 인스턴스 선택을 제안하였다. 제안하는 인스턴스 선택은 T-S 퍼지 모델에서의 가중 평균 역퍼지화와 통계학에서 사용하는 정규분포의 신뢰구간과 같은 구간 선택을 이용하여 인스턴스를 선택하였다. 제안하는 인스턴스 선택의 분류 성능을 평가하기 위해서 인스턴스 사용 전/후에 따라서 분류 성능을 비교하였다. 인스턴스 사용 전/후에 따른 분류 성능은 각각 77.33%, 78.19%로 나타났다. 또한 인스턴스 사용 전/후에 따른 분류 성능 간에 차이점을 보여주기 위해서 통계학에서 사용하는 맥니마 검정을 사용하였다. 맥니마 검정의 결과로 유의 확률이 0.05보다 적게 나오므로 인스턴스 선택의 분류 성능이 인스턴스 선택을 하지 않는 경우의 분류 성능보다 우수함을 확인 할 수가 있었다.

Keywords

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