Affiliations: [a]
College of Information Technology, United Arab Emirates University Al Ain, UAE
| [b]
Toronto Rehabilitation Institute, University Health Network Toronto, Canada
| [c]
Department of Computer Science, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Correspondence:
[*]
Corresponding author. Amir Ahmad, College of Information Technology, United Arab Emirates University Al Ain, 15551, UAE. E-mail: amirahmad@uaeu.ac.ae.
Abstract: Regression via Classification (RvC) is a process to solve a regression problem by using a classifier. An ensemble consists of many models, in which the final result is the combination of the results of these individual models. In this paper, two RvC ensemble methods are proposed. In the first ensemble method, the output of the ensemble method is modified to achieve the final output. A formula is derived in this paper for this purpose. In the second method, a new approach is proposed to compute the output of each model of an ensemble. It is shown that an accurate binary classifier can be transformed into an accurate regression method with the proposed methods. It is also shown experimentally, by using popular Random Forests as a classifier in the proposed ensemble methods against Random Forests as a regression method, the effectiveness of the proposed RvC ensemble methods.
