Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions


The KIPS Transactions:PartB , Vol. 11, No. 6, pp. 717-722, Oct. 2004
10.3745/KIPSTB.2004.11.6.717,   PDF Download:

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.set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representingset 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 ofset 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%.


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Cite this article
[IEEE Style]
J. S. Lim, "Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions," The KIPS Transactions:PartB , vol. 11, no. 6, pp. 717-722, 2004. DOI: 10.3745/KIPSTB.2004.11.6.717.

[ACM Style]
Joon Shik Lim. 2004. Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions. The KIPS Transactions:PartB , 11, 6, (2004), 717-722. DOI: 10.3745/KIPSTB.2004.11.6.717.