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Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds
arXiv: Learning (2021)
  • Hieu D. Nguyen, Rowan University
  • Mohammed Sarosh Khan, Rowan University
  • Nicholas Kaegi, Rowan University
  • Shen-Shyang Ho, Rowan University
  • Jonathan Moore, Rowan University
  • Logan Borys, Rowan University
  • Lucas Lavalva, Rowan University
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 under the assumption that all base classifiers are independent and the second under the assumption 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.


Publication Date
September 18, 2021
Citation Information
Hieu D. Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen-Shyang Ho, et al.. "Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds" arXiv: Learning (2021)
Available at: http://works.bepress.com/hieu-nguyen/8/
Creative Commons license
Creative Commons License
This work is licensed under a Creative Commons CC_BY-NC-ND International License.