The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithms is the error rate and not the AUC value. We give a detailed statistical analysis of the relationship between the AUC and the error rate, including the first exact expression of the expected value an...

Motivation The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the d...

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A well established technique to improve the classification performances is to combine more classifiers. However, 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, an effective instrument to analyze the d...

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC ...

The Schlumberger Cluster (donated by Schlumberger Inc.) delivers high performance computing, grid infrastructure, very large data storage, and advanced visualization services through the School of Sciences and Engineering at the American University in Cairo. It is a production cluster that provides cost-effective computational power to a wide range of scientific, engineering, and interdisciplin...