An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

DOI

10.2991/icsnce-18.2018.34How to use a DOI?

Keywords

Machine Learning; Ensemble Learning; Text Classification

Abstract

Ensemble learning can improve the accuracy of the classification algorithm and it has been widely used. Traditional ensemble learning methods include bagging, boosting and other methods, both of which are ensemble learning methods based on homogenous base classifiers, and obtain a diversity of base classifiers only through sample perturbation. However, heterogenous base classifiers tend to be more diverse, and multi-angle disturbances tend to obtain a variety of base classifiers.This paper presents a text classification ensemble learning method based on multi-angle perturbation heterogeneous base classifier, and validates the effectiveness of the algorithm through experiments.

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  - Fan Huimin
AU  - Li Pengpeng
AU  - Zhao Yingze
AU  - Li Danyang
PY  - 2018/04
DA  - 2018/04
TI  - An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 171
EP  - 175
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.34
DO  - 10.2991/icsnce-18.2018.34
ID  - Huimin2018/04
ER  -