A decision-tree framework for instance-space decomposition

Shahar Cohen, Lior Rokach, Oded Maimon

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This paper presents a novel instance-space decomposition framework for decision trees. According to this framework, the original instance-space is decomposed into several subspaces in a parallel-to-axis manner. A different classifier is assigned to each subspace. Subsequently, an unlabelled instance is classified by employing the appropriate classifier based on the subspace where the instance belongs. An experimental study which was conducted in order to compare various implementations of this framework indicates that previously presented implementations can be improved both in terms of accuracy and computation time.

Original languageEnglish
Title of host publicationAdvances in Web Intelligence and Data Mining
EditorsMark Last, Piotr Szczepaniak, Piotr Szczepaniak, Zeev Vlvolkov, Abraham Kandel
Number of pages10
StatePublished - 27 Sep 2006

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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