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All issues Volume 255 (2019) MATEC Web Conf., 255 (2019) 02004 References
Open Access
Issue
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
Article Number 02004
Number of page(s) 7
Section Smart Manufacturing and Industrial 4.0
DOI https://doi.org/10.1051/matecconf/201925502004
Published online 16 January 2019
  1. J. Yang and V. Honavar, Feature Subset Selection Using a Genetic Algorithm, (1997). [Google Scholar]
  2. D. Whitley, A genetic algorithm tutorial, Stat. Comput. 4, 65–85 (1994). [CrossRef] [Google Scholar]
  3. L. Zhuo, J. Zheng, F. Wang, X. Li, B. Ai, and J. Qian, A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 37, 397–402 (2008). [Google Scholar]
  4. I. Guyon, A. Elisseeff, and A. M. De, An Introduction to Variable and Feature Selection, J. Mach. Learn. Res. 3, 1157–1182 (2003). [Google Scholar]
  5. G. Forman, An Extensive Empirical Study of Feature Selection Metrics for Text Classification, J. Mach. Learn. Res. 3, 1289–1305 (2003). [Google Scholar]
  6. F. B. Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, E. W. Steyerberg, and D. Khalili, A tutorial on variable selection for clinical prediction models: Feature selection methods in data-mining could improve the results, J. Clin. Epidemiol. 71, 76–85 (2015). [CrossRef] [Google Scholar]
  7. M. Tutkan, M. C. Ganiz, and S. Akyokuş, Helmholtz principle based supervised and unsupervised feature selection methods for text mining, Inf. Process. Manag. 52, 885–910 (2016). [CrossRef] [Google Scholar]
  8. V. Aksakalli and M. Malekipirbazari, Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation, Pattern Recognit. Lett. 75, 41–47 (2015). [CrossRef] [Google Scholar]
  9. S. Jiang and L. Wang, Efficient feature selection based on correlation measure between continuous and discrete features, Inf. Process. Lett. 116, 203–215 (2016). [CrossRef] [Google Scholar]
  10. A. Moayedikia, K.-L. Ong, Y. L. Boo, W. G. Yeoh, and R. Jensen, Feature selection for high dimensional imbalanced class data using harmony search, Eng. Appl. Artif. Intell. 57, 38–49 (2017). [CrossRef] [Google Scholar]
  11. A. Senawi, H. L. Wei, and S. A. Billings, A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking, Pattern Recognit. 67, 47–61 (2017). [CrossRef] [Google Scholar]
  12. R. Kohavi and H. John, Artificial Intelligence Wrappers for feature subset selection, Artif. Intell. 97, 273–324 (1997). [CrossRef] [Google Scholar]
  13. X. Zhang, G. Wu, Z. Dong, and C. Crawford, Embedded feature-selection support vector machine for driving pattern recognition, J. Franklin Inst. 352, 669–685 (2015). [CrossRef] [Google Scholar]
  14. R. Saravanan, P. Asokan, and M. Sachidanandam, A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations, Int. J. Mach. Tools Manuf. 42, 1327–1334 (2002). [CrossRef] [Google Scholar]
  15. H. Karimi and F. Yousefi, Application of artificial neural network-genetic algorithm (ANN-GA) to correlation of density in nanofluids, Fluid Phase Equilib. 336, 79–83 (2012). [CrossRef] [Google Scholar]
  16. K. Dasgupta, J. K. Mondal, and P. Dutta, Optimized Video Steganography Using Genetic Algorithm (GA), Procedia Technol. 10, 131–137 (2013). [CrossRef] [Google Scholar]
  17. R. Leardi and A. L. Gonzalez, Genetic algorithms applied to feature selection in PLS regression-how and when to use them, Chemom. Intell. Lab. Syst. 41, 195–207 (1998). [CrossRef] [Google Scholar]
  18. C. L. Huang and C. J. Wang, A GA-based feature selection and parameters optimizationfor support vector machines, Expert Syst. Appl. 31, 231–240 (2006). [CrossRef] [Google Scholar]
  19. L. Wang, G. Xu, J. Wang, S. Yang, M. Guo, and W. Yan, Motor Imagery BCI Research Based on Sample Entropy and SVM, in 2012 6th Int. Conf. Electromagn. F. Probl. Appl. ICEF’2012, (2012). [Google Scholar]
  20. M. Asghari Oskoei and H. Hu, Myoelectric control systems-A survey, Biomed. Signal Process. Control 2, 275–294 (2007). [CrossRef] [Google Scholar]
  21. R. M. Luque, D. Elizondo, E. Lopez-Rubio, and E. J. Palomo, GA-based feature selection approach in biometric hand systems, Proc. Int. Jt. Conf. Neural Networks, 246–253 (2011). [Google Scholar]
  22. C. De Stefano, F. Fontanella, C. Marrocco, and Scotto Di Freca, A GA-based feature selection approach with an application to handwritten character recognition, Pattern Recognit. Lett. 35, 130–141 (2014). [CrossRef] [Google Scholar]
  23. B. Oluleye, A. Leisa, J. Leng, and D. Dean, A Genetic Algorithm-Based Feature Selection, Int. J. Electron. Commun. Comput. Eng. 5, 899–905 (2014). [Google Scholar]
  24. P. Moradi and M. Gholampour, A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy, Appl. Soft Comput. 43, 117–130 (2016). [CrossRef] [Google Scholar]
  25. K. A. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptative Systems (1975). [Google Scholar]
  26. M. Mitchell, An Introduction to Genetic Algorithms (1999). [Google Scholar]

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