Instance selection using one-versus-all and one-versus-one decomposition approaches in multiclass classification datasets

Ching Lin Fang, Ming Chang Wang, Chih Fong Tsai, Wei Chao Lin*, Pei Qi Liao

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations

Abstract

Instance is important in data analysis and mining; it filters out unrepresentative, redundant, or noisy data from a given training set to obtain effective model learning. Various instance selection algorithms are proposed in the literature, and their potential and applicability in data cleaning and preprocessing steps are demonstrated. For multiclass classification datasets, the existing instance selection algorithms must deal with all the instances across the different classes simultaneously to produce a reduced training set. Generally, every multiclass classification dataset can be regarded as a complex domain problem, which can be effectively solved using the divide-and-conquer principle. In this study, the one-versus-all (OVA) and one-versus-one (OVO) decomposition approaches were used to decompose a multiclass dataset into multiple binary class datasets. These approaches have been widely employed when constructing the classifier but have never been considered in instance selection. The results of instance selection performance obtained with the OVA, OVO, and baseline approaches were assessed and compared for 20 different domain multiclass datasets as the first study and five medical domain datasets as the validation study. Furthermore, three instance selection algorithms were compared, including IB3, DROP3, and GA. The results demonstrate that using the OVO approach to perform instance selection can make the support vector machine (SVM) and k-nearest neighbour (k-NN) classifiers perform significantly better than the OVA and baseline approaches in terms of the area under the ROC curve (AUC) rate, regardless of the instance selection algorithm used. Moreover, the OVO approach can provide reasonably good data reduction rates and processing times, which are all better than those of the OVA approach.

Original languageEnglish
Article numbere13217
JournalExpert Systems
Volume40
Issue number6
DOIs
StatePublished - 07 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 John Wiley & Sons Ltd.

Keywords

  • data mining
  • instance selection
  • machine learning
  • multiclass classification
  • one-versus-all
  • one-versus-one

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