The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions

가중 퍼지 소속함수 기반 신경망을 이용한 Wisconsin Breast Cancer 예측 퍼지규칙의 추출

  • 임준식 (경원대학교 소프트웨어학부)
  • Published : 2004.10.01
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Abstract

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NNWFM)을 이용하여 Wisconsin breast cancer의 예측을 수행하는 퍼지규칙을 추출하고 있다. NNWFM는 자기적응적(self adaptive)가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이론 기반으로 예측을 수행한다. 신경망 구조의 중간 부분인 하이퍼박스(hyperbox)들은 n개의 대, 중, 소의 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합은 퍼지집합의 bounded sum을 사용하여 다시 하나의 가중 퍼지소속함수로 합성된다. n개의 특징입력(feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. NNWFM으로 추출된 2개의 퍼지규칙은 99.41%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다.

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