MTBR: Multi-Target Boosting for Regression

Sangdi Lin, Bahareh Azarnoush, George C. Runger

Research output: Contribution to journalArticlepeer-review

Abstract

Gradient boosting method has been successfully used for single target prediction problems. In real world applications, however, problems involving the prediction of multiple target attributes are often of interest. In this paper, a multi-target boosting method for regression problems, named as MTBR, is proposed. Although MTBR builds one model for each target attribute separately, all the target attributes are utilized when building each model. In each boosting iteration, the base learner, the regression tree in particular, is learned by selecting the best models from all the target attributes. We also introduce a novel knowledge transfer approach. That is, the tree structure learned from one target attribute, representing a way to partition the feature space, is used to predict another target attribute. Experiments with six data sets compare MTBR to other ensemble regression methods, and prove the effectiveness of MTBR in leveraging the knowledge of multiple target attributes and improving the model accuracy.

Original languageEnglish (US)
Article number8770154
Pages (from-to)626-636
Number of pages11
JournalIEEE Transactions on Knowledge and Data Engineering
Volume33
Issue number2
DOIs
StatePublished - Feb 1 2021

Keywords

  • ensemble method
  • gradient boosting tree
  • Multi-target learning
  • regression
  • transfer learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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