An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers
- 10.2991/icsnce-18.2018.34How to use a DOI?
- Machine Learning; Ensemble Learning; Text Classification
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.
- © 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/).
Cite this article
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 -