Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of dichotomizers for maximizing the partial area under the ROC curve
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. Several combination rules have been proposed based on maximizing the accuracy and the Area under the ROC curve (AUC). Taking into account that there are several applications which focus only on a part of the ROC curve, i.e. the one most relevant for the problem, we recently proposed a new algorithm aimed at finding the linear combination of dichotomizers which maximizes only the interesting part of the AUC. Since the algorithm uses a greedy approach, in this paper we define and evaluate some possible strategies which select the dichotomizers to combine at each step of the greedy approach. An experimental comparison is drawn on a multibiometric database.