AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?
Robert Kyle Eichelberger, Victor S. Sheng

Last modified: 2013-06-29


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.


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

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