Novel ensemble methods for regression via classification problems

Amir Ahmad, Sami M. Halawani, Ibrahim A. Albidewi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to covert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we use a discretization method, Extreme Randomized Discretization (ERD), in which bin boundaries are created randomly to create ensembles. We present two ensemble methods for RvC problems. We show theoretically that the proposed ensembles for RvC perform better than RvC with the equal-width discretization method. We also show the superiority of the proposed ensemble methods experimentally. Experimental results suggest that the proposed ensembles perform competitively to the method developed specifically for regression problems.

Original languageEnglish
Pages (from-to)6396-6401
Number of pages6
JournalExpert Systems with Applications
Volume39
Issue number7
DOIs
Publication statusPublished - Jun 1 2012
Externally publishedYes

Keywords

  • Classification
  • Decision trees
  • Ensembles
  • Regression

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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