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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Selecting Proper Multi-Class SVM Training Methods

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Yawen Chen

South China University of Technology


Zeyi Wen

National University of Singapore


Jian Chen

South China University of Technology


Jin Huang

South China Normal University


DOI:

10.1609/aaai.v32i1.12156


Abstract:

Support Vector Machines (SVMs) are excellent candidate solutions to solving multi-class problems, and multi-class SVMs can be trained by several different methods. Different training methods commonly produce SVMs with different effectiveness, and no multi-class SVM training method always outperforms other multi-class SVM training methods on all problems. This raises difficulty for practitioners to choose the best training method for a given problem. In this work, we propose a Multi-class Method Selection (MMS) approach to help users select the most appropriate method among one-versus-one (OVO), one-versus-all (OVA) and structural SVMs (SSVMs) for a given problem. Our key idea is to select the training method based on the distribution of training data and the similarity between different classes. Using the distribution and class similarity, we estimate the unclassifiable rate of each multi-class SVM training method, and select the training method with the minimum unclassifiable rate. Our initial findings show: (i) SSVMs with linear kernel perform worse than OVO and OVA; (ii) MMS often produces SVM classifiers that can confidently classify unseen instances.

Topics: AAAI

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Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang Selecting Proper Multi-Class SVM Training Methods Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang Selecting Proper Multi-Class SVM Training Methods AAAI 2018, .

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang (2018). Selecting Proper Multi-Class SVM Training Methods. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. Selecting Proper Multi-Class SVM Training Methods. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. 2018. Selecting Proper Multi-Class SVM Training Methods. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. (2018) "Selecting Proper Multi-Class SVM Training Methods", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang, "Selecting Proper Multi-Class SVM Training Methods", AAAI, p., 2018.

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. "Selecting Proper Multi-Class SVM Training Methods". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. "Selecting Proper Multi-Class SVM Training Methods". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Yawen Chen||Zeyi Wen||Jian Chen||Jin Huang. Selecting Proper Multi-Class SVM Training Methods. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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