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Related Experiment Videos

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes.

Oriol Pujol1, Petia Radeva, Jordi Vitrià

  • 1Departament de Matemática Aplicada i Análisi, Universitat de Barcelona, Gran Via 585, Barcelona, 08007, Spain. oriol@maia.ub.es

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 27, 2006
PubMed
Summary

This study introduces a new heuristic method for learning error correcting output codes (ECOC) matrices. The approach uses a hierarchical class partition to maximize discrimination, creating compact matrices for improved classification.

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Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Computer Vision

Background:

  • Error Correcting Output Codes (ECOC) are widely used for multi-class classification.
  • Traditional ECOC methods often focus on codeword separation, potentially limiting discriminative power.
  • A need exists for ECOC matrices that prioritize class discrimination.

Purpose of the Study:

  • To develop a novel heuristic method for learning ECOC matrices.
  • To maximize class discrimination within a hierarchical partition of the class space.
  • To create compact ECOC matrices with enhanced discrimination capabilities.

Main Methods:

  • A heuristic approach for learning ECOC matrices was developed.
  • A hierarchical partition of the class space was employed, guided by a discriminative criterion.

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  • A binary tree structure was utilized for creating the hierarchical partition.
  • Optimal codeword separation was intentionally traded for increased class discrimination.

    • Main Results:

    • A compact ECOC matrix with high discrimination power was successfully obtained.
    • The method demonstrated effectiveness in validation using the UCI database.
    • The approach was successfully applied to the real-world problem of traffic sign image classification.

    Conclusions:

    • The proposed heuristic method effectively learns discriminative ECOC matrices.
    • Hierarchical partitioning offers a viable strategy for enhancing ECOC performance.
    • The method shows promise for applications in image classification and other pattern recognition tasks.