Publication:
A Bayes risk minimization machine for example-dependent cost classification

Loading...
Thumbnail Image
Identifiers
Publication date
2021-07
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
A new method for example-dependent cost (EDC) classification is proposed. The method constitutes an extension of a recently introduced training algorithm for neural networks. The surrogate cost function is an estimate of the Bayesian risk, where the estimates of the conditional probabilities for each class are defined in terms of a 1-D Parzen window estimator of the output of (discriminative) neural networks. This probability density is modeled with the objective of allowing an easy minimization of a sampled version of the Bayes risk. The conditional probabilities included in the definition of the risk are not explicitly estimated, but the risk is minimized by a gradient-descent algorithm. The proposed method has been evaluated using linear classifiers and neural networks, with both shallow (a single hidden layer) and deep (multiple hidden layers) architectures. The experimental results show the potential and flexibility of the proposed method, which can handle EDC classification under imbalanced data situations that commonly appear in this kind of problems.
Description
Keywords
Bayesian risk, Example-dependent cost (EDC), Imbalanced classification, Parzen windows
Bibliographic citation
Lazaro, M. & Figueiras-Vidal, A. R. (2021). A Bayes Risk Minimization Machine for Example-Dependent Cost Classification. IEEE Transactions on Cybernetics, 51(7), 3524–3534.