Decision-tree instance-space decomposition with grouped gain-ratio

Shahar Cohen, Lior Rokach, Oded Maimon

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

This paper examines a decision-tree framework for instance-space decomposition. According to the framework, the original instance-space is hierarchically partitioned into multiple subspaces and a distinct classifier is assigned to each subspace. Subsequently, an unlabeled, previously-unseen instance is classified by employing the classifier that was assigned to the subspace to which the instance belongs. After describing the framework, the paper suggests a novel splitting-rule for the framework and presents an experimental study, which was conducted, to compare various implementations of the framework. The study indicates that using the novel splitting-rule, previously presented implementations of the framework, can be improved in terms of accuracy and computation time.

Original languageEnglish
Pages (from-to)3592-3612
Number of pages21
JournalInformation Sciences
Volume177
Issue number17
DOIs
StatePublished - 1 Sep 2007

Keywords

  • Classification
  • Decision-trees
  • Instance-space decomposition
  • Multiple-classifier systems

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