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Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

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8 Author(s)
Pablo González ; Artificial Intelligence Center, University of Oviedo, Gijón, Spain ; Eva Álvarez ; Jose Barranquero ; Jorge Díez
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In many applications, the mistakes made by an automatic classifier are not equal, they have different costs. These problems may be solved using a cost-sensitive learning approach. The main idea is not to minimize the number of errors, but the total cost produced by such mistakes. This brief presents a new multiclass cost-sensitive algorithm, in which each example has attached its corresponding misclassification cost. Our proposal is theoretically well-founded and is designed to optimize cost-sensitive loss functions. This research was motivated by a real-world problem, the biomass estimation of several plankton taxonomic groups. In this particular application, our method improves the performance of traditional multiclass classification approaches that optimize the accuracy.

Published in:

IEEE Transactions on Neural Networks and Learning Systems  (Volume:24 ,  Issue: 11 )