| Issue |
MATEC Web Conf.
Volume 201, 2018
2017 The 3rd International Conference on Inventions (ICI 2017)
|
|
|---|---|---|
| Article Number | 05004 | |
| Number of page(s) | 7 | |
| Section | Invention of numerical scheme and application | |
| DOI | https://doi.org/10.1051/matecconf/201820105004 | |
| Published online | 14 September 2018 | |
Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications
1
Institute of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan
2
National Taichung University of Science and Technology, Taichung 411, Taiwan
3
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
4
Department of Electrical and Control Engineering National Chiao Tung University, Hsinchu 300, Taiwan
5
Faculty of Engineering and Information Technology University of Technology Sydney, Sydney 2007, Australia
* Corresponding author: cjlin@ncut.edu.tw; Tel.: +886-953-002-825
In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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