A System for Induction of Oblique Decision Trees

Sreerama K. Murthy; Simon Kasif; Steven L. Salzberg

doi:10.1613/jair.63

Abstract

This article describes a new system for induction ofoblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a goodoblique split (in the form of a hyperplane) at each node of a decisiontree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We presentextensive empirical studies, using both real and artificial data, thatanalyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examinethe benefits of randomization for the construction of oblique decisiontrees.

Keywords

Oblique caseHyperplaneDecision treeComputer scienceConstruct (python library)Node (physics)Tree (set theory)Artificial intelligenceAlgorithmTheoretical computer scienceMathematicsCombinatorics

Affiliated Institutions

Johns Hopkins University US

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Publication Info

Year: 1994
Type: article
Volume: 2
Pages: 1-32
Citations: 572
Access: Closed

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APA Style

                            
                                    Sreerama K. Murthy, 
                                
                                    Simon Kasif, 
                                
                                    Steven L. Salzberg
                                
                            (1994). 
                            A System for Induction of Oblique Decision Trees. 
                            Journal of Artificial Intelligence Research
                            , 2
                            
                            , 1-32.
                            https://doi.org/10.1613/jair.63

Identifiers

DOI: 10.1613/jair.63