New algorithms for learning and pruning oblique decision trees

Shah, Shesha and Sastry, PS (1999) New algorithms for learning and pruning oblique decision trees. In: IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 29 (4). pp. 494-505.

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Abstract

In this paper, we present methods for learning and pruning oblique decision trees, We propose a new function for evaluating different split rules at each node while growing the decision tree. Unlike the other evaluation functions currently used in literature (which are all based on some notion of purity of a node), this new evaluation function is based on the concept of degree of linear separability, We adopt a correlation-based optimization technique called the Alopex algorithm for finding the split rule that optimizes our evaluation function at each node. The algorithm we present here is applicable only for 2-class problems. Through empirical studies, we demonstrate that our algorithm learns good compact- decision trees. We suggest a representation scheme for oblique decision trees that makes explicit the fact that an oblique decision tree represents each class as a union of convex sets bounded by hyperplanes in the feature space. Using this representation, we present a new pruning technique. Unlike other pruning techniques, which generally replace heuristically selected subtrees of the original tree by leaves, our method can radically restructure the decision tree. Through empirical investigation, we demonstrate the effectiveness of our method.

Item Type:	Journal Article
Publication:	IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews
Publisher:	IEEE
Additional Information:	©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	Learning;linear separability;optimization;tree induction;tree pruning
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	25 Aug 2008
Last Modified:	19 Sep 2010 04:15
URI:	http://eprints.iisc.ac.in/id/eprint/1647

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