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Authors: Anderson Rocha and Siome Goldenstein

Affiliation: University of Campinas (Unicamp), Brazil

ISBN: 978-989-8111-69-2

Keyword(s): Multi-class classification, Error correcting output codes, ECOC, Affine Bayes, Bayesian approach.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Statistical Approach

Abstract: Several researchers have proposed effective approaches for binary classification in the last years. We can easily extend some of those techniques to multi-class. Notwithstanding, some other powerful classifiers (e.g., SVMs) are hard to extend to multi-class. In such cases, the usual approach is to reduce the multi-class problem complexity into simpler binary classification problems (divide-and-conquer). In this paper, we address the multi-class problem by introducing the concept of affine relations among binary classifiers (dichotomies), and present a principled way to find groups of high correlated base learners. Finally, we devise a strategy to reduce the number of required dichotomies in the overall multi-class process.

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Paper citation in several formats:
Rocha A.; Goldenstein S. and (2009). MULTI-CLASS FROM BINARY - Divide to conquer.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 323-330. DOI: 10.5220/0001777803230330

@conference{visapp09,
author={Anderson Rocha and Siome Goldenstein},
title={MULTI-CLASS FROM BINARY - Divide to conquer},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001777803230330},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - MULTI-CLASS FROM BINARY - Divide to conquer
SN - 978-989-8111-69-2
AU - Rocha, A.
AU - Goldenstein, S.
PY - 2009
SP - 323
EP - 330
DO - 10.5220/0001777803230330

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