Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
479126 | European Journal of Operational Research | 2007 | 13 Pages |
Abstract
A recently developed data separation/classification method, called isotonic separation, is applied to breast cancer prediction. Two breast cancer data sets, one with clean and sufficient data and the other with insufficient data, are used for the study and the results are compared against those of decision tree induction methods, linear programming discrimination methods, learning vector quantization, support vector machines, adaptive boosting, and other methods. The experiment results show that isotonic separation is a viable and useful tool for data classification in the medical domain.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Young U. Ryu, R. Chandrasekaran, Varghese S. Jacob,