Random projection random discretization ensembles - Ensembles of linear multivariate decision trees

Amir Ahmad, Gavin Brown

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

In this paper, we present a novel ensemble method random projection random discretization ensembles(RPRDE) to create ensembles of linear multivariate decision trees by using a univariate decision tree algorithm. The present method combines the better computational complexity of a univariate decision tree algorithm with the better representational power of linear multivariate decision trees. We develop random discretization (RD) method that creates random discretized features from continuous features. Random projection (RP) is used to create new features that are linear combinations of original features. A new dataset is created by augmenting discretized features (created by using RD) with features created by using RP. Each decision tree of a RPRD ensemble is trained on one dataset from the pool of these datasets by using a univariate decision tree algorithm. As these multivariate decision trees (because of features created by RP) have more representational power than univariate decision trees, we expect accurate decision trees in the ensemble. Diverse training datasets ensure diverse decision trees in the ensemble. We study the performance of RPRDE against other popular ensemble techniques using C4.5 tree as the base classifier. RPRDE matches or outperforms other popular ensemble methods. Experiments results also suggest that the proposed method is quite robust to the class noise.

Original languageEnglish
Article number6574846
Pages (from-to)1225-1239
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number5
DOIs
Publication statusPublished - May 2014
Externally publishedYes

Keywords

  • Ensembles
  • decision trees
  • discretization
  • noise
  • random projections
  • randomization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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