Binarization with Boosting and Oversampling for Multiclass Classification

Ayon SEN, Md Monirul ISLAM, Kazuyuki MURASE, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

38 Citations (Scopus)

Abstract

Using a set of binary classifiers to solve multiclass classification problems has been a popular approach over the years. The decision boundaries learnt by binary classifiers (also called base classifiers) are much simpler than those learnt by multiclass classifiers. This paper proposes a new classification framework, termed binarization with boosting and oversampling (BBO), for efficiently solving multiclass classification problems. The new framework is devised based on the one-versus-all (OVA) binarization technique. Unlike most previous work, BBO employs boosting for solving the hard-to-learn instances and oversampling for handling the class-imbalance problem arising due to OVA binarization. These two features make BBO different from other existing works. Our new framework has been tested extensively on several multiclass supervised and semi-supervised classification problems using five different base classifiers, including neural networks, C4.5, k-nearest neighbor, repeated incremental pruning to produce error reduction, support vector machine, random forest, and learning with local and global consistency. Experimental results show that BBO can exhibit better performance compared to its counterparts on supervised and semi-supervised classification problems. © 2015 IEEE.
Original languageEnglish
Article number7098396
Pages (from-to)1078-1091
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume46
Issue number5
Early online date30 Apr 2015
DOIs
Publication statusPublished - May 2016
Externally publishedYes

Keywords

  • Binarization
  • boosting
  • multiclass classification
  • Oversampling

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