<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Ensemble learning can improve the accuracy of the classification algorithm and it has been widely used. Traditional ensemble learning methods include bagging,...

An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Publicado en línea: 07 may 2018

Páginas: 130 - 134

DOI: https://doi.org/10.21307/ijanmc-2018-021

Palabras clave
Machine Learning, Ensemble Learning, Text Classification

© 2018 Fan Huimin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

TABLE I.

TABLE II.

TABLE III.

TABLE IV.

TABLE V.

TABLE VI.

TABLE VII.

An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Publicado en línea: 07 may 2018

Páginas: 130 - 134

DOI: https://doi.org/10.21307/ijanmc-2018-021

Palabras claveMachine Learning, Ensemble Learning, Text Classification

© 2018 Fan Huimin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

TABLE I.

TABLE II.

TABLE III.

TABLE IV.

TABLE V.

TABLE VI.

TABLE VII.

Palabras clave
Machine Learning, Ensemble Learning, Text Classification