@inproceedings{5df2948bd6304f2ea047c424f3e7195b,
title = "Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds",
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. ",
author = "Nguyen, {Hieu D.} and Khan, {Mohammed Sarosh} and Nicholas Kaegi and Ho, {Shen Shyang} and Jonathan Moore and Logan Borys and Lucas Lavalva",
note = "Funding Information: ACKNOWLEDGMENT The authors would like to acknowledge partial financial support from the Center for Undergraduate Research in Mathematics (CURM) through NSF grant DMS-1722563. Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021 ; Conference date: 01-11-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/ICTAI52525.2021.00114",
language = "English (US)",
series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE Computer Society",
pages = "719--723",
booktitle = "Proceedings - 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, ICTAI 2021",
address = "United States",
}