Proper Losses for Learning with ExampleDependent Costs
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Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 94:5266, 2018.
Abstract
We study the design of costsensitive learning algorithms with exampledependent costs, when cost matrices for each example are given both during training and test. The approach is based on the empirical risk minimization framework, where we replace the standard loss function by a combination of surrogate losses belonging to the family of proper losses. The actual contribution of each example to the risk is then given by a loss that depends on the cost matrix for the specific example. We then evaluate the use of such exampledependent loss functions in realworld binary and multiclass problems, namely credit risk assessment and musical genre classification. Using different neural network architectures, we show that with the appropriate choice of the exampledependent losses, we can outperform conventional costsensitive methods in terms of total cost, making a more efficient use of cost information during training and test as compared to existing discriminative approaches.
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