Research Article Open Access

A Novel Ensemble Method for Regression via Classification Problems

Sami M. Halawani1, Ibrahim A. Albidewi1 and Amir Ahmad1
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Journal of Computer Science
Volume 7 No. 3, 2011, 387-393

DOI: https://doi.org/10.3844/jcssp.2011.387.393

Submitted On: 14 December 2010 Published On: 7 March 2011

How to Cite: Halawani, S. M., Albidewi, I. A. & Ahmad, A. (2011). A Novel Ensemble Method for Regression via Classification Problems. Journal of Computer Science, 7(3), 387-393. https://doi.org/10.3844/jcssp.2011.387.393

Abstract

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.

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Keywords

  • Regression via Classification (RvC)
  • ERD ensembles
  • classification problem
  • decision trees
  • Extreme Randomized Discretization (ERD)
  • Monothetic Contrast Criteria (MCC)
  • RvC perform
  • Mean Square Error (MSE)
  • neural network