title: Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.
abstract: Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.
authors.affiliation: Department of Computing, Federal University of Technology-Paraná, Cornélio Procópio, Brazil.
authors.forename: Priscila T M
authors.initials: PT
authors.lastname: Saito
authors.affiliation: Big Data Brazil, São Paulo, SP, Brazil.
authors.forename: Rodrigo Y M
authors.initials: RY
authors.lastname: Nakamura
authors.affiliation: Institute of Computing, Federal University of Mato Grosso do Sul, Campo Grande, Brazil.
authors.forename: Willian P
authors.initials: WP
authors.lastname: Amorim
authors.affiliation: Department of Computing, São Paulo State University, Bauru, Brazil.
authors.forename: João P
authors.initials: JP
authors.lastname: Papa
authors.affiliation: Institute of Computing, University of Campinas, Campinas, SP, Brazil.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: de Rezende
authors.affiliation: Institute of Computing, University of Campinas, Campinas, SP, Brazil.
authors.forename: Alexandre X
authors.initials: AX
authors.lastname: Falcão
mesh_terms: D000465:Algorithms; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated