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An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Authors
  • Huimin, Fan1
  • Pengpeng, Li1
  • Yingze, Zhao2
  • Danyang, Li1
  • 1 School of Computer Science and Engineering Xi’an Technological University, Xi’an, 710021 , (China)
  • 2 School of School of Marxism Xi’an Jiaotong University, Xi’an , (China)
Type
Published Article
Journal
International Journal of Advanced Network, Monitoring and Controls
Publisher
Exeley Inc.
Publication Date
Jan 01, 2018
Volume
3
Issue
1
Pages
130–134
Identifiers
DOI: 10.21307/ijanmc-2018-021
Source
Exeley
Keywords
License
Green

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.

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