Learning regression problems by using classifiers

Amir Ahmad, Shehroz S. Khan, Ajay Kumar

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)945-955
Number of pages11
JournalJournal of Intelligent and Fuzzy Systems
Volume35
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • Randomization
  • Regression
  • discretization
  • ensembles
  • regression trees

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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