IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0129947.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129947
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0129947&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0129947?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    2. Mariane Barros Neiva & Patrick Guidotti & Odemir Martinez Bruno, 2018. "Enhancing LBP by preprocessing via anisotropic diffusion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-29, August.
    3. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    4. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    5. Ferraz de Arruda, Henrique & Reia, Sandro Martinelli & Silva, Filipi Nascimento & Amancio, Diego Raphael & da Fontoura Costa, Luciano, 2022. "Finding contrasting patterns in rhythmic properties between prose and poetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    6. Diego R. Amancio & Osvaldo N. Oliveira jr & Luciano F. Costa, 2015. "Topological-collaborative approach for disambiguating authors’ names in collaborative networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 465-485, January.
    7. Nguyen Minh Tien & Cyril Labbé, 2018. "Detecting automatically generated sentences with grammatical structure similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1247-1271, August.
    8. Yu-Tso Chen & Chi-Hua Chen & Szu Wu & Chi-Chun Lo, 2018. "A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features," Mathematics, MDPI, vol. 7(1), pages 1-17, December.
    9. Ranjit Panigrahi & Samarjeet Borah & Akash Kumar Bhoi & Muhammad Fazal Ijaz & Moumita Pramanik & Rutvij H. Jhaveri & Chiranji Lal Chowdhary, 2021. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research," Mathematics, MDPI, vol. 9(6), pages 1-32, March.
    10. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    11. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    12. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    13. Guerreiro, Lucas & Silva, Filipi N. & Amancio, Diego R., 2024. "Recovering network topology and dynamics from sequences: A machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0129947. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.