Pruning multivariate decision trees by hyperplane merging

Miroslav Kubat, Doris Flotzinger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Several techniques for induction of multivariate decision trees have been published in the last couple of years. Internal nodes of such trees typically contain binary tests questioning to what side of a hyperplane the example lies. Most of these algorithms use cut-off pruning mechanisms similar to those of traditional decision trees. Nearly unexplored remains the large domain of substitutional pruning methods, where a new decision test (derived from previous decision tests) replaces a subtree. This paper presents an approach to multivariate-tree pruning based on merging the decision hyperplanes, and demonstrates its performance on artificial and benchmark data.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML-95 - 8th European Conference on Machine Learning, 1995, Proceedings
EditorsNada Lavrac, Stefan Wrobel
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540592865, 9783540592860
StatePublished - 1995
Externally publishedYes
Event8th European Conference on Machine Learning, ECML 1995 - Heraclion, Greece
Duration: Apr 25 1995Apr 27 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th European Conference on Machine Learning, ECML 1995

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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