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

Author

Listed:
  • Priscila T M Saito
  • Rodrigo Y M Nakamura
  • Willian P Amorim
  • João P Papa
  • Pedro J de Rezende
  • Alexandre X Falcão

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.

Suggested Citation

  • Priscila T M Saito & Rodrigo Y M Nakamura & Willian P Amorim & João P Papa & Pedro J de Rezende & Alexandre X Falcão, 2015. "Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0129947
    DOI: 10.1371/journal.pone.0129947
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    References listed on IDEAS

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    1. Diego Raphael Amancio & Cesar Henrique Comin & Dalcimar Casanova & Gonzalo Travieso & Odemir Martinez Bruno & Francisco Aparecido Rodrigues & Luciano da Fontoura Costa, 2014. "A Systematic Comparison of Supervised Classifiers," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
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