Issue No. 07 - July (2010 vol. 32)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.72
Sarunas Raudys , Vilnius University, Vilnius
Aistis Raudys , Vilnius University, Vilnius
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.
Cost-sensitive learning, loss function, pairwise classification, perceptron.
A. Raudys and S. Raudys, "Pairwise Costs in Multiclass Perceptrons," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1324-1328, 2010.