A Comparative Study of Fuzzy Classifiers on Breast Cancer Data

Authors:
Ravi. Jain;Ajith. Abraham
Affiliations:
School of Information Technology, James Cook University (Cairns Campus), Smithfield, Australia 4878;Computer Science Department, Oklahoma State University (Tulsa Campus), Tulsa, Oklahoma, USA 74106
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

In this paper, we examine and compare the performance of four fuzzy rule generation methods on Wisconsin breast cancer data [2]. These methods were reported by Ishibuchi [1]et al. For the diagnosis of breast cancer, the determination of the presence of benign/malignantbreast tumors represents a very complex problem (even for an experienced cytologist). The goal of this paper is to compare and contrast fuzzy rule generation methods on breast cancer data that involve no time-consuming tuning procedures. Since The performance of each approach for test patterns (i.e., the generalization of ability of each approach) is evaluated by cross validation techniques on breast cancer data sets.