MULTI-CLASS FROM BINARY - Divide to conquer

Anderson Rocha, Siome Goldenstein

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 Harvard Style

Rocha A. and Goldenstein S. (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


in Bibtex Style

@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},
}


in EndNote Style

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