A Text Classification Algorithm Based On RS

DOI

10.2991/icmmct-17.2017.22How to use a DOI?

Keywords

Rough set, Meta-feature selection, Attribute reduction, Text classification

Abstract

Study a variety of text feature extraction methods, through mutual information(MI), document frequency(DF),information gain(IG) and 2 statistics(CHI) algorithm, using of their respective advantages complementary, proposed a kind of multiple combination feature extraction algorithm based on rough set(RS-MCFEA);First using attribute reduction based on rough set in keeping the classification ability under the condition of constant fast will text feature space dimension reduction, and then by multiple combinations of features extracted in the feature space after the dimension reduction is more representative of characteristic items, filter out some representative weak feature items, finally using SVM classifier to classify text; The experimental results show that this algorithm can effectively improve text classification accuracy and efficiency of classification.

Copyright

© 2017, 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  - JianLin Li
PY  - 2017/04
DA  - 2017/04
TI  - A Text Classification Algorithm Based On RS
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 105
EP  - 108
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.22
DO  - 10.2991/icmmct-17.2017.22
ID  - Li2017/04
ER  -