In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. The most part of combination rules are based on maximizing the accuracy and, only recently, the Area under the ROC curve (AUC) has been proposed as an alternative measure. However, there are several applications which focus only on particular regions of the ROC curve, i.e. the most relevant for the problem. In these cases, looking on a partial section of the AUC is the most suitable approach to adopt. In this paper we propose a new algorithm able to maximize only a part of the AUC by means of a linear combination of dichotomizers. Moreover, we empirically show that algorithms that maximize the AUC do not maximize the partial AUC, i.e., the two kinds of maximization are independent.
Combination of dichotomizers for maximizing the partial area under the roc curve
RICAMATO, Maria Teresa;TORTORELLA, Francesco
2010-01-01
Abstract
In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. The most part of combination rules are based on maximizing the accuracy and, only recently, the Area under the ROC curve (AUC) has been proposed as an alternative measure. However, there are several applications which focus only on particular regions of the ROC curve, i.e. the most relevant for the problem. In these cases, looking on a partial section of the AUC is the most suitable approach to adopt. In this paper we propose a new algorithm able to maximize only a part of the AUC by means of a linear combination of dichotomizers. Moreover, we empirically show that algorithms that maximize the AUC do not maximize the partial AUC, i.e., the two kinds of maximization are independent.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
