Using Gini-Index for Feature Selection in Text Categorization

DOI

10.2991/icibet-14.2014.22How to use a DOI?

Keywords

text categorization, feature selection, Gini-Index, feature selection function

Abstract

With the rapid development of World Wide Web, text categorization has played an important role in organizing and processing large amount of text data. The first and major problem of text categorization is how to select the best subset from the original high feature space in order to reduce the high dimensionality of the original feature space and improve the classification performance. We aim to use improved Gini-index for text feature selection, constructing the measure function based on Gini-Index. We compare it to other four feature selection measures using two kinds of classifiers on two different document corpus. The result of experiments shows that its performance is comparable with other text feature selection approaches. However, it is perfect in the time complexity of algorithm.

Copyright

© 2014, 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  - Weidong Zhu
AU  - Jingyu Feng
AU  - Yongmin Lin
PY  - 2014/02
DA  - 2014/02
TI  - Using Gini-Index for Feature Selection in Text Categorization
BT  - Proceedings of the 2014 International Conference on Information, Business and Education Technology
PB  - Atlantis Press
SP  - 76
EP  - 80
SN  - 1951-6851
UR  - https://doi.org/10.2991/icibet-14.2014.22
DO  - 10.2991/icibet-14.2014.22
ID  - Zhu2014/02
ER  -