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https://hdl.handle.net/10356/101838
Title: | Empirical comparison of bagging-based ensemble classifiers | Authors: | Suganthan, P. N. Ye, Ren |
Keywords: | DRNTU::Engineering::Electrical and electronic engineering | Issue Date: | 2012 | Source: | Ye, R., & Suganthan, P.N. (2012). Empirical comparison of bagging-based ensemble classifiers. 2012 15th International Conference on Information Fusion (FUSION), 917-924. | Conference: | International Conference on Information Fusion (FUSION) (15th : 2012) | Abstract: | This paper compares empirically four bagging-based ensemble classifiers, namely the ensemble adaptive neuro-fuzzy inference system (ANFIS), the ensemble support vector machine (SVM), the ensemble extreme learning machine (ELM) and the random forest. The comparison of these four ensemble classifiers is novel because it has not been reported in the existing literature. The classifiers are evaluated with thirteen binary class datasets and the empirical results show that the ensemble methods employed in the four ensemble classifiers boost the testing accuracy by 1-5% on average from their base classifiers. In addition, the testing accuracy can be improved by increasing the number of base classifiers. The empirical results also show that the bagging SVM is the most favorable ensemble classifier among them. | URI: | https://hdl.handle.net/10356/101838 http://hdl.handle.net/10220/19784 |
URL: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289900 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2012 International Society of Information Fusion. This paper was published in 2012 15th International Conference on Information Fusion (FUSION) and is made available as an electronic reprint (preprint) with permission of International Society of Information Fusion. The paper can be found at the following official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289900. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Conference Papers |
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