Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. ,  are interested in the performance of both one-against-all and one-against-one for classification algorithms, such as decision tree, naive 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. Hover, 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,  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.
['R. Kyle Eichelberger', 'Victor S. Sheng']
I am writing a research paper on a idea Does one-against-all or one-against-one improve the performance of multiclass classifications?
[{"from": "human", "value": "I am writing a research paper on a idea Does one-against-all or one-against-one improve the performance of multiclass classifications?"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: One-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. ,  are interested in the performance of both one-against-all and one-against-one for classification algorithms, such as decision tree, naive 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. Hover, 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,  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."}]