Document Type

Article

Version Deposited

Submitted for publication (PrePrint)

Publication Date

9-18-2021

Publication Title

arXiv

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.

Comments

This is a preprint deposited in arXiv with a CC-BY-NC-ND license.

Share

COinS