Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint

Saito, Priscila T. M.; Nakamura, Rodrigo Y. M.; Amorim, Willian P.; Papa, Joao P. [UNESP]; Rezende, Pedro J. de; Falcao, Alexandre X.

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint

Arquivos

WOS000358147500046.PDF (2.12 MB)

Data

2015-06-26

Autores

Saito, Priscila T. M.

Nakamura, Rodrigo Y. M.

Editor

Public Library Science

Tipo

Artigo

Direito de acesso

Acesso aberto

Resumo

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.