Comparing Multi-objective and Threshold-moving ROC Curve Generation for a Prototype-based Classifier

Abstract

Receiver Operating Characteristics (ROC) curves represent the performance of a classifier for all 
possible operating conditions, i.e., for all preferences regarding the tradeoff between false positives 
and false negatives. The generation of a ROC curve generally involves the training of a single classifier 
for a given set of operating conditions, with the subsequent use of threshold-moving to obtain a 
complete ROC curve. Recent work has shown that the generation of ROC curves may also be formulated 
as a multi-objective optimization problem in ROC space: the goals to be minimized are the false positive 
and false negative rates. This technique also produces a single ROC curve, but the curve may derive from 
operating points for a number of different classifiers. This paper aims to provide an empirical comparison 
of the performance of both of the above approaches, for the specific case of prototype-based classifiers. 
Results on synthetic and real domains shows a performance advantage for the multi-objective approach.