title: Multiclass support vector machines with example-dependent costs applied to plankton biomass estimation.
abstract: 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.
authors.forename: Pablo
authors.initials: P
authors.lastname: González
authors.forename: Eva
authors.initials: E
authors.lastname: Álvarez
authors.forename: Jose
authors.initials: J
authors.lastname: Barranquero
authors.forename: Jorge
authors.initials: J
authors.lastname: Díez
authors.forename: Rafael
authors.initials: R
authors.lastname: González-Quirós
authors.forename: Enrique
authors.initials: E
authors.lastname: Nogueira
authors.forename: Ángel
authors.initials: Á
authors.lastname: López-Urrutia
authors.forename: Juan José
authors.initials: JJ
authors.lastname: del Coz
mesh_terms: D000465:Algorithms; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D010933:Plankton / Q000145:classification* / Q000166:cytology* / Q000302:isolation & purification; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine