An Improved Naïve Bayes Classifier for Large Scale Text

DOI

10.2991/icaita-18.2018.9How to use a DOI?

Keywords

text classification; Naïve Bayes; words frequency; semantic analysis; parallel computing

Abstract

Naïve Bayes classifiers is widely used for text classification because of its simplicity and effectiveness. In this paper, an improved Naïve Bayes classifiers was proposed, using multinomial model to modify its rough parameter estimation and parallel competing with MapReduce to categories to text documents. The experimental results show that the proposed method is able to improve the accuracy of Naïve Bayes classifiers dramatically, and has good scalability and extensibility for large-scale text classification.

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/).

Download article (PDF)

TY  - CONF
AU  - Huaixin Chen
AU  - Daocai Fu
PY  - 2018/03
DA  - 2018/03
TI  - An Improved Naïve Bayes Classifier for Large Scale Text
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 33
EP  - 36
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.9
DO  - 10.2991/icaita-18.2018.9
ID  - Chen2018/03
ER  -