Improved term selection algorithm based on variance in text categorization

DOI

10.2991/icsem.2013.157How to use a DOI?

Keywords

variance, text classification, term selection

Abstract

This article improves the algorithm of term weighting in automated text classification. The traditional TFIDF algorithm is a common method that is used to measure term weighting in text classification However, the algorithm does not take the distribution of terms in inter-class. In order to solve the problem, variance which describes the distribution of terms in inter-class and intra-class is used to revise TFIDF algorithm. This article mainly researched about the construction of LFHW term sets and new approaches to term weighting, These new approaches are also applied to the hierarchical classification system Compared with traditional TFIDF algorithm the results of simulation experiment have demonstrated that the improved TFIDF algorithm can get better classification results

Copyright

© 2013, 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/).

Download article (PDF)

TY  - CONF
AU  - Ran Li
AU  - Xianjiu Guo
PY  - 2013/04
DA  - 2013/04
TI  - Improved term selection algorithm based on variance in text categorization
BT  - Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM 2013)
PB  - Atlantis Press
SP  - 765
EP  - 768
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsem.2013.157
DO  - 10.2991/icsem.2013.157
ID  - Li2013/04
ER  -