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Cost-Sensitive Classification for Medical Diagnosis

Cost-Sensitive Classification for Medical Diagnosis

Gerald Schaefer, Tomoharu Nakashima, Yasuyuki Yokota
Copyright: © 2008 |Pages: 6
ISBN13: 9781599048895|ISBN10: 1599048892|EISBN13: 9781599048901
DOI: 10.4018/978-1-59904-889-5.ch040
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MLA

Schaefer, Gerald, et al. "Cost-Sensitive Classification for Medical Diagnosis." Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, IGI Global, 2008, pp. 297-302. https://doi.org/10.4018/978-1-59904-889-5.ch040

APA

Schaefer, G., Nakashima, T., & Yokota, Y. (2008). Cost-Sensitive Classification for Medical Diagnosis. In N. Wickramasinghe & E. Geisler (Eds.), Encyclopedia of Healthcare Information Systems (pp. 297-302). IGI Global. https://doi.org/10.4018/978-1-59904-889-5.ch040

Chicago

Schaefer, Gerald, Tomoharu Nakashima, and Yasuyuki Yokota. "Cost-Sensitive Classification for Medical Diagnosis." In Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, 297-302. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-889-5.ch040

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Abstract

In this article, we present a cost-sensitive approach to medical diagnosis based on fuzzy rule-based classification (Schaefer, Nakashima, Yokota, & Ishibuchi, 2007). While fuzzy rule-based systems have been mainly employed for control problems (Lee, 1990) more recently they have also been applied to pattern classification problems (Ishibuchi & Nakashima, 1999; Nozaki, Ishibuchi, & Tanaka, 1996). We modify a fuzzy rule-based classifier to incorporate the concept of weight which can be considered as the cost of an input pattern being misclassified. The pattern classification problem is thus reformulated as a cost minimisation problem. Based on experimental results on the Wisconsin breast cancer dataset, we demonstrate the efficacy of our approach. We also show that the application of a learning algorithm can further improve the classification performance of our classifier.

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