An empirical study of reducing multiclass classification methodologies

R. Kyle Eichelberger, Victor S. Sheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


One-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. In this paper, we are interested in the performance of both one-against-all and one-against-one for basic classification algorithms, such as decision tree, naïve bayes, support vector machine, and logistic regression. Since both one-against-all and one-against-one work like creating a classification committee, they are expected to improve the performance of classification algorithms. However, our experimental results surprisingly show that one-against-all worsens the performance of the algorithms on most datasets. One-against-one helps, but performs worse than the same iterations of bagging these algorithms. Thus, we conclude that both one-against-all and one-against-one should not be used for the algorithms that can perform multiclass classifications directly. Bagging is an better approach for improving their performance.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 9th International Conference, MLDM 2013, Proceedings
Number of pages15
StatePublished - 2013
Event9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013 - New York, NY, United States
Duration: Jul 19 2013Jul 25 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7988 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013
Country/TerritoryUnited States
CityNew York, NY


  • All-at-Once
  • C4.5
  • Logistic Regression
  • Naive Bayes
  • One-Against-All
  • One-Against-One
  • SVM
  • multiclass classification


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