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http://dx.doi.org/10.5391/JKIIS.2005.15.5.599

Extracting Minimized Feature Input And Fuzzy Rules Using A Fuzzy Neural Network And Non-Overlap Area Distribution Measurement Method  

Lim Joon-Shik (Department of E-Commerce Software, Kyungwon University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.5, 2005 , pp. 599-604 More about this Journal
Abstract
This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer 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 n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.
Keywords
퍼지 신경망;규칙 추출;가중 퍼지 소속함수;비중복 분산 면적 측정법;
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