Optimal N-ary ECOC Matrices for Ensemble Classification

Hieu D. Nguyen, Lucas J. Lavalva, Shen Shyang Hot, Mohammed Sarosh Khan, Nicholas Kaegi

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

Abstract

A new recursive construction of $N$ -ary error-correcting output code (ECOC) matrices for ensemble classification methods is presented, generalizing the classic doubling construction for binary Hadamard matrices. Given any prime integer $N$, this deterministic construction generates base- $N$ symmetric square matrices $M$ of prime-power dimension having optimal minimum Hamming distance between any two of its rows and columns. Experimental results for six datasets demonstrate that using these deterministic coding matrices for $N$ -ary ECOC classification yields comparable and in many cases higher accuracy compared to using randomly generated coding matrices. This is particular true when $N$ is adaptively chosen so that the dimension of $M$ matches closely with the number of classes in a dataset, which reduces the loss in minimum Hamming distance when $M$ is truncated to fit the dataset. This is verified through a distance formula for $M$ which shows that these adaptive matrices have significantly higher minimum Hamming distance in comparison to randomly generated ones.

Original languageEnglish (US)
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728190488
DOIs
StatePublished - 2021
Event2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Orlando, United States
Duration: Dec 5 2021Dec 7 2021

Publication series

Name2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings

Conference

Conference2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021
Country/TerritoryUnited States
CityOrlando
Period12/5/2112/7/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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