Year: 2021
Type: conference-paper
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.
Authors: Hieu D. Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen-Shyang Ho, Jonathan Moore, Logan Borys, Lucas Lavalva
Institution: Rowan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3199205344