A novel ensemble method for regression via classification problems

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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Problem statement: 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. Approach: In this study, we use a discretization method, Extreme Randomized Discretization (ERD), in which bin boundaries are created randomly to create ensembles. Results: We show that the proposed ensemble method is useful 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 method experimentally. Experimental results suggest that the proposed ensembles perform competitively to the method developed specifically for regression problems. Conclusion: As the proposed method is independent of the choice of the classifier, various classifiers can be used with the proposed method to solve the regression method.

Original languageEnglish
Pages (from-to)387-393
Number of pages7
JournalJournal of Computer Science
Issue number3
Publication statusPublished - 2011
Externally publishedYes


  • Classification problem
  • Decision trees
  • ERD ensembles
  • Extreme randomized discretization (ERD)
  • Mean square error (MSE)
  • Monothetic Contrast Criteria (MCC)
  • Neural network
  • Regression via Classification (RVC)
  • Rvc perform

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence


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