Making large-scale support vector machine learning practical
Advances in kernel methods
Robust Classification for Imprecise Environments
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
Combination of Experts by Classifiers in Similarity Score Spaces
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Score Fusion by Maximizing the Area under the ROC Curve
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Index driven combination of multiple biometric experts for AUC maximisation
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An overview of AI research in Italy
Artificial intelligence
Partial AUC maximization in a linear combination of dichotomizers
Pattern Recognition
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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.