A Bayes Risk Minimization Machine for Example-Dependent Cost Classification

A new method for example-dependent cost (EDC) classification is proposed. The constitutes an extension of a recently introduced training algorithm neural networks. surrogate function estimate the Bayesian risk, where estimates conditional probabilities each class are defined in terms 1-D Parzen window estimator output (discriminative) This probability density modeled with objective allowing easy minimization sampled version Bayes risk. included definition risk not explicitly estimated, but minimized by gradient-descent algorithm. proposed has been evaluated using linear classifiers and networks, both shallow (a single hidden layer) deep (multiple layers) architectures. experimental results show potential flexibility method, which can handle EDC under imbalanced data situations that commonly appear this kind problems.