@conference { 773, title = {Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method}, booktitle = {Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)}, volume = {5632}, year = {2009}, note = {
}, pages = {163-177}, publisher = {Springer}, organization = {Springer}, address = {Leipzig, Germany}, abstract = {
In two-class score-based problems the combination of scores from an ensemble of experts is generally used to obtain distributions for positive and negative patterns that exhibit a larger degree of separation than those of the scores to be combined. Typically, combination is carried out by a \"static\" linear combination of scores, where the weights are computed by maximising a performance function. These weights are equal for all the patterns, as they are assigned to each of the expert to be combined. In this paper we propose a \"dynamic\" formulation where the weights are computed individually for each pattern. Reported results on a biometric dataset show the effectiveness of the proposed combination methodology with respect to \"static\" linear combinations and trained combination rules.
}, keywords = {AUC, bio00, bio02, mcs00, mcs01, ROC, score combination}, author = {Roberto Tronci and Giorgio Giacinto and Fabio Roli}, editor = {Petra Perner} }