Novel ensemble methods for regression via classification problems

Authors:
Amir Ahmad;Sami M. Halawani;Ibrahim A. Albidewi
Affiliations:
Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia;Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia;Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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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.