An Ensemble Method for Multi-class and Multi-label Text Categorization

DOI

10.2991/iske.2007.147How to use a DOI?

Keywords

Text categorization, Twin-SVM, Naïve Bayes, Ensemble of classifiers, Multi-label and multi-class

Abstract

A method for multi-class and multi-label automated text categorization based on twin-SVM with naïve Bayes ensemble is proposed. Twin-SVM classifiers give a solution to the multi-label problem. For multi-class situation, naïve Bayes classifier constrains the belonging scope of a testing sample within a few most likely classes and greatly reduces the number of binary classifiers needed to make the final prediction. The benefits of the ensemble method are described and preliminary results with Reuters-21578 data set are also presented.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo-Feng Zhang
AU  - Xin Xu
AU  - Jinshu Su
PY  - 2007/10
DA  - 2007/10
TI  - An Ensemble Method for Multi-class and Multi-label Text Categorization
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 862
EP  - 865
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.147
DO  - 10.2991/iske.2007.147
ID  - Zhang2007/10
ER  -