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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.06.031 Byline: Young U. Ryu (a), R. Chandrasekaran (b), Varghese S. Jacob (c) Keywords: Data mining; Isotonic separation; Breast cancer diagnosis 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. Author Affiliation: (a) School of Management, The University of Texas at Dallas, P.O. Box 830688, SM 33, Richardson, TX 75083-0688, USA (b) School of Engineering and Computer Science, The University of Texas at Dallas, P.O. Box 830688, Richardson, TX 75083-0688, USA (c) School of Management, The University of Texas at Dallas, P.O. Box 830688, SM 40, Richardson, TX 75083-0688, USA Article History: Received 18 February 2005; Accepted 5 June 2006

Data classification and prediction problems are prevalent in many domains. The need to predict to which class a particular data point belongs has been seen in areas such as medical […]

The alternative formulations and algorithms are developed based on a dynamic programming approach and are generalized for series-parallel/parallel-series (S/P) reducible systems. The objective of the discrete choice-set is to ensure minimal cost for achieving a specified reliability for the systems under consideration.