Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?

Authors

  • Robert Eichelberger University of Central Arkansas
  • Victor Sheng Department of Computer Science, University of Central Arkansas

DOI:

https://doi.org/10.1609/aaai.v27i1.8522

Keywords:

multi-class classification, One-Against-All, One-Against-One, All-at-Once

Abstract

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 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 better approach for improving their performance.

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Published

2013-06-29

How to Cite

Eichelberger, R., & Sheng, V. (2013). Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 1609-1610. https://doi.org/10.1609/aaai.v27i1.8522