Making large-scale support vector machine learning practical
Advances in kernel methods
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The combination of classifiers is an established technique to improve the classification performance. The 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). This paper presents a method for the linear combination of two-class classifiers aimed at maximizing the AUC. The effectiveness of the approach has been confirmed by the tests performed on standard datasets.