Pairwise costs in multiclass perceptrons.
A novel loss function to train a net of K single-layer perceptrons (KSLPs) is suggested, where pairwise misclassification cost matrix can be incorporated directly. The complexity of the network remains the same; a gradient's computation of the loss function does not necessitate additional calculations. Minimization of the loss requires a smaller number of training epochs. Efficacy of cost-sensitive methods depends on the cost matrix, the overlap of the pattern classes, and sample sizes. Experiments with real-world pattern recognition (PR) tasks show that employment of novel loss function usually outperforms three benchmark methods.
Raudys
Sarunas
S
Department of Mathematics and Informatics, Vilnius University, Didlaukio 47, Vilnius LT-08303, Lithuania. sarunas.raudys@mif.vu.lt
Raudys
Aistis
A