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Performance Evaluation of Error-Correcting Output Coding Based on Noisy and Noiseless Binary Classifiers.

Gendo Kumoi1, Hideki Yagi2, Manabu Kobayashi1

  • 1Center for Data Science, Waseda University, 1-6-1, Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan.

International Journal of Neural Systems
|January 10, 2023
PubMed
Summary
This summary is machine-generated.

Error-correcting output coding (ECOC) uses binary classifiers to build multi-valued classifiers. This study theoretically analyzes ECOC, finding that Hamming distance in the codeword table is key for performance with both noisy and noiseless classifiers.

Keywords:
Multi-valued classificationerror-correcting output codingestimated posterior probabilityhamming distancenoiseless binary classifiernoisy binary classifier

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Area of Science:

  • Machine Learning
  • Computer Science
  • Information Theory

Background:

  • Error-correcting output coding (ECOC) constructs multi-valued classifiers from binary classifiers.
  • ECOC leverages coding theory to correct errors from individual classifiers.
  • Experimental results show ECOC performs well on real-world data.

Purpose of the Study:

  • To provide a theoretical analysis of Error-correcting output coding (ECOC).
  • To investigate the optimality of ECOC codeword tables with noiseless binary classifiers.
  • To analyze the error rate of ECOC with noisy binary classifiers.

Main Methods:

  • Defined noisy (error-prone) and noiseless (accurate) binary classifiers.
  • Theoretically analyzed codeword table optimality for noiseless classifiers.
  • Theoretically analyzed error rates for noisy classifiers.

Main Results:

  • Identified the Hamming distance of the codeword table as a critical performance indicator.
  • Demonstrated the importance of codeword table structure for ECOC accuracy.
  • Provided theoretical insights into ECOC performance under varying classifier noise levels.

Conclusions:

  • The Hamming distance of the codeword table is a crucial factor for ECOC performance.
  • Theoretical analysis provides a deeper understanding of ECOC beyond experimental observations.
  • This work lays the foundation for further theoretical investigations into ECOC.