An Intelligent Feature Selection and Classification Method Based on Hybrid ABC–SVM
By:
References
- Vapnik, Vladimir N. The Nature of Statistical Learning Theory [M]// The nature of statistical learning theory /. Springer, 2000:988 - 999.
- Zhang X, Zhou J, Wang C, et al. Multi-class support vector machine optimized by intercluster distance and self-adaptive deferential evolution. Applied Mathematics & Computation, 2012, 218(9):4973-4987.
- Shen Z, Chen X, Zhang X, et al. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM Measurement, 2012, 45(1):30-40.
- Chen Z, Li J, Wei L. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Artificial Intelligence in Medicine, 2007, 41(2):161-175.
- Widodo A, Yang B S. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Systems with Applications, 2008, 35(1-2):307-316.
- Xiang X, Zhou J, An X, et al. Fault diagnosis based on Walsh transforms and support vector machine. Mechanical Systems & Signal Processing, 2008, 22(7):1685-1693.
- Gao X, Liu F. Robust visual tracking based on support vector machine and weighted sampling method. International Journal on Smart Sensing & Intelligent Systems, 2015, 8(1):255-271.
- Zhang K, Li Y, Scarf P, et al. Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks Neurocomputing, 2011, 74(17):2941-2952.
- Anne E, Alip B, Stephen B, et al. Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm-support vector machines: HGASVM. Expert Systems with Applications, 2009, 36(2):1391-1402.
- Lin S W, Lee Z J, Chen S C, et al. Parameter determination of support vector machine and feature selection using simulated annealing approach Applied Soft Computing, 2008, 8(4):1505- 1512.
- Huang C L. ACO-based hybrid classification system with feature subset selection and model parameters optimization Neurocomputing, 2009, 73(1–3):438-448.
- Widiyanto D, Purnomo D M J, Jati G, et al. Modification of particle swarm optimization by reforming global best term to accelerate the searching of odor sources. International Journal on Smart Sensing & Intelligent Systems, 2016, 9(3):1412-1430.
- Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm Applied Soft Computing, 2008, 8(1):687-697.
- Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm Applied Mathematics & Computation, 2009, 214(1):108-132.
- Lei Y, He Z, Zi Y. A new approach to intelligent fault diagnosis of rotating machinery Expert Systems with Applications, 2008, 35(4):1593-1600.
- Hsu C W, Lin C J. Errata to “A comparison of methods for multiclass support vector machines”.. IEEE Transactions on Neural Networks, 2002, 13(4):415-425.
- Xu C, Duan H, Liu F. Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning. Aerospace Science & Technology, 2010, 14(8):535-541.
- C.-W. Hsu, C.-C. Chang, C.-J. Lin. “A Practical Guide to Support Vector Classification”, <http://www.csie.ntu.edu.tw/_cjlin/libsvm/>, 2009.
- K. A. Loparo, “Bearings Vibration Data Set”, Case Western Reserve University [EB/OL]<http://www.eecs.case.edu/laboratory/bearing/welcome_overview.htm>
DOI: https://doi.org/10.21307/ijssis-2017-943 | Journal eISSN: 1178-5608
Language: English
Page range: 1859 - 1876
Submitted on: Jul 17, 2016
Accepted on: Oct 16, 2016
Published on: Dec 1, 2016
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2016 Jie Li, Qiuwen Zhang, Zhang Yongzhi, Li Chang, Xiao Jian, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.