An IG-RS-SVM classifier for analyzing reviews of E-commerce product

DOI

10.2991/icitmi-15.2015.98How to use a DOI?

Keywords

e-commerce; feature selection; ensemble learning; support vector machine

Abstract

Analyzing reviews of E-commerce product is a kind of text classification which belongs to supervised learning. Due to the huge number of words, high dimensional feature space is a serious problem in text classification. In order to solve it, a new algorithm, IG-RS-SVM, is proposed. Information Gain (IG) is a feature selection algorithm which can reduce the dimension of feature subspace. Random subspace, a kind of ensemble learning algorithm, can divide the feature space to smaller ones each submitted to a base classifier such as Support Vector Machine (SVM). After experiments, it shows that IG-RS-SVM algorithm can effectively improve the text classification accuracy.

Copyright

© 2015, 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  - Jiajun Ye
AU  - Huan Ren
AU  - Hangxia Zhou
PY  - 2015/10
DA  - 2015/10
TI  - An IG-RS-SVM classifier for analyzing reviews of E-commerce product
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 601
EP  - 606
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.98
DO  - 10.2991/icitmi-15.2015.98
ID  - Ye2015/10
ER  -