Document Type
Article
Publication Date
1-29-2019
Publication Title
Mathematical Problems in Engineering
Publisher
Hindawi Publishing Corporation
Volume
2019
First page number:
1
Last page number:
17
Abstract
Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy.
Disciplines
Data Storage Systems | Environmental Public Health
File Format
File Size
1.798 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Zhang, H.,
Song, Y.,
Jiang, B.,
Chen, B.,
Shan, G.
(2019).
Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance.
Mathematical Problems in Engineering, 2019
1-17.
Hindawi Publishing Corporation.
http://dx.doi.org/10.1155/2019/8906034