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- W2180568734 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." @default.
- W2180568734 created "2016-06-24" @default.
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- W2180568734 date "2013-06-29" @default.
- W2180568734 modified "2023-10-14" @default.
- W2180568734 title "Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?" @default.
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- W2180568734 doi "https://doi.org/10.1609/aaai.v27i1.8522" @default.
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