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This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.
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Tom
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1--13
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tab., wykr., bibliogr. 23 poz.
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- Institute for Computer Science. Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee Geb. 79, D-79110 Freiburg i.Br.,Germany, skramer@informatik.uni-freiburg.de
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bwmeta1.element.baztech-article-BUS2-0003-0053