Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

A Novel Multiclass Text Classification Algorithm Based on Multiconlitron

Authors
Yuping Qin, Fengfeng Qiu, Qiangkui Leng, Aihua Zhang
Corresponding Author
Yuping Qin
Available Online April 2016.
DOI
https://doi.org/10.2991/emim-16.2016.173How to use a DOI?
Keywords
Multiconlitron; Multiclass classification; One-against-one; Support vector machines
Abstract
A novel multiclass text classification algorithm based on multiconlitron is proposed. The multiconlitron is constructed for each possible pair of classes in sample space, each of which is used to separate two classes. For the sample to be classified, every multiconlitron is used to judge its class,and vote for the corresponding class. The final class of the sample is determined by the number of votes. The classification experiments are done on Reuters 21578, and the experimental results show that the proposed algorithm has better performance. Compared with multiclass support vector machines 1-a-1, 1-a-r and DAGSVM, while ensuring the classification accuracy, it improves the classification speed and training speed greatly.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
6th International Conference on Electronic, Mechanical, Information and Management Society
Part of series
Advances in Computer Science Research
Publication Date
April 2016
ISBN
978-94-6252-176-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/emim-16.2016.173How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yuping Qin
AU  - Fengfeng Qiu
AU  - Qiangkui Leng
AU  - Aihua Zhang
PY  - 2016/04
DA  - 2016/04
TI  - A Novel Multiclass Text Classification Algorithm Based on Multiconlitron
BT  - 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 843
EP  - 848
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.173
DO  - https://doi.org/10.2991/emim-16.2016.173
ID  - Qin2016/04
ER  -