Classifier combination is a useful and common methodology to design an effective classification system. A large number of combination rules has been proposed hitherto, mostly aimed at minimizing the error rate. Recently, some methods have been presented that are devoted to maximize the area under th
Exploiting AUC for optimal linear combinations of dichotomizers
β Scribed by Claudio Marrocco; Mario Molinara; Francesco Tortorella
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 203 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0167-8655
No coin nor oath required. For personal study only.
β¦ Synopsis
The combination of classifiers is an established technique to improve the classification performance. The possible combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two-class problems. In this case, a good alternative is given by the area under the receiver operating characteristic curve (AUC), whose effectiveness in measuring the classification quality has been proved in many recent papers.
In this paper, we propose a method to achieve the optimal linear combination of two dichotomizers based on the maximization of the AUC of the resulting classification system. The effectiveness of the approach has been confirmed by the tests performed on standard datasets.
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