An improved text classifier based on random forest algorithm - comparative studies on multiple text classifiers

DOI

10.2991/macmc-17.2018.39How to use a DOI?

Keywords

Natural language processing; Learning algorithm; Random forest; Artificial intelligence

Abstract

Various classifiers have sprung up in recent years. This paper introduces a new intelligent algorithm for text categorization based on improved random forest algorithm. This improvement greatly increases the performance of the original random forest algorithm. The classifier was tested on the reuters-21578 data set and its classification effect was obtained. The classifier is compared with traditional principle similar classifier CART, REPTree and J48. The experimental results show that the classification accuracy of text classifier based on improved random forest algorithm is higher, and it is faster.

Copyright

© 2018, 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  - Xin Luo
PY  - 2018/01
DA  - 2018/01
TI  - An improved text classifier based on random forest algorithm - comparative studies on multiple text classifiers
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 175
EP  - 178
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.39
DO  - 10.2991/macmc-17.2018.39
ID  - Luo2018/01
ER  -