Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds

Hieu D. Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen Shyang Ho, Jonathan Moore, Logan Borys, Lucas Lavalva

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first assumes that all base classifiers are independent and the second assumes that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, ICTAI 2021
PublisherIEEE Computer Society
Pages719-723
Number of pages5
ISBN (Electronic)9781665408981
DOIs
StatePublished - 2021
Event33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021 - Virtual, Online, United States
Duration: Nov 1 2021Nov 3 2021

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2021-November
ISSN (Print)1082-3409

Conference

Conference33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period11/1/2111/3/21

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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