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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 25 / No. 1: Twenty-Fifth AAAI Conference on Artificial Intelligence

Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems

March 8, 2023

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Authors

Ke Tang

University of Science and Technology of China


Rui Wang

University of Science and Technology of China


Tianshi Chen

Chinese Academy of Sciences


DOI:

10.1609/aaai.v25i1.7901


Abstract:

The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment. MAUC, the extension of AUC to multi-class problems, has also attracted a lot of attention. However, despite the emergence of approaches for training classifiers with large AUC, little has been done for MAUC. This paper analyzes MAUC in-depth, and reveals that the maximization of MAUC can be achieved by decomposing the multi-class problem into a number of independent sub-problems. These sub-problems are formulated in the form of a “learning to rank” problem, for which well-established methods already exist. Based on the analysis, a method that employs RankBoost algorithm as the sub-problem solver is proposed to achieve classification systems with maximum MAUC. Empirical studies have shown the advantages of the proposed method over other eight relevant methods. Due to the importance of MAUC to multi-class cost-sensitive learning and class imbalanced learning problems, the proposed method is a general technique for both problems. It can also be generalized to accommodate other learning algorithms as the sub-problem solvers.

Topics: AAAI

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HOW TO CITE:

Ke Tang|| Rui Wang|| Tianshi Chen Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems Proceedings of the AAAI Conference on Artificial Intelligence, 25 (2011) 483.

Ke Tang|| Rui Wang|| Tianshi Chen Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems AAAI 2011, 483.

Ke Tang|| Rui Wang|| Tianshi Chen (2011). Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 25, 483.

Ke Tang|| Rui Wang|| Tianshi Chen. Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 25 2011 p.483.

Ke Tang|| Rui Wang|| Tianshi Chen. 2011. Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems. "Proceedings of the AAAI Conference on Artificial Intelligence, 25". 483.

Ke Tang|| Rui Wang|| Tianshi Chen. (2011) "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems", Proceedings of the AAAI Conference on Artificial Intelligence, 25, p.483

Ke Tang|| Rui Wang|| Tianshi Chen, "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems", AAAI, p.483, 2011.

Ke Tang|| Rui Wang|| Tianshi Chen. "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems". Proceedings of the AAAI Conference on Artificial Intelligence, 25, 2011, p.483.

Ke Tang|| Rui Wang|| Tianshi Chen. "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems". Proceedings of the AAAI Conference on Artificial Intelligence, 25, (2011): 483.

Ke Tang|| Rui Wang|| Tianshi Chen. Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems. AAAI[Internet]. 2011[cited 2023]; 483.


ISSN: 2374-3468


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