Isotonic Separation

Authors:
R. Chandrasekaran;Young U. Ryu;Varghese S. Jacob;Sungchul Hong
Affiliations:
School of Engineering and Computer Science, P.O. Box 830688, The University of Texas at Dallas, Richardson, Texas 75083-0688, USA;School of Management, P.O. Box 830688, SM 33, The University of Texas at Dallas, Richardson, Texas 75083-0688, USA;School of Management, P.O. Box 830688, SM 40, The University of Texas at Dallas, Richardson, Texas 75083-0688, USA;Department of Computer Science and Information Sciences, Towson University, 8000 York Road, Stephens Hall, Room 314, Towson, Maryland 21252, USA
Venue:
INFORMS Journal on Computing
Year:
2005

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Abstract

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 diagnosis, credit rating, Web filtering, prediction, and stock rating. This has led to strong interest in developing systems that can accurately classify data and predict outcome. The classification is typically based on the feature values of objects being classified. Often, a form of ordering relation, defined by feature values, on the objects to be classified is known. For instance, the objects belonging to one class have larger (or smaller) feature values than do those in the other class. Exploiting this characteristic of isotonicity, we propose a data-classification method called isotonic separation based on linear programming, especially network programming. The paper also addresses an extension of the isotonic-separation method for continuous outcome prediction. Applications of the isotonic separation for discrete outcome prediction and its extension for continuous outcome prediction are shown to illustrate its applicability.