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). [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). [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). [Google Scholar]
  8. V. Aksakalli and M. Malekipirbazari, Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation, Pattern Recognit. Lett. 75, 41–47 (2015). [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). [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). [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). [Google Scholar]
  12. R. Kohavi and H. John, Artificial Intelligence Wrappers for feature subset selection, Artif. Intell. 97, 273–324 (1997). [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). [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). [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). [Google Scholar]
  16. K. Dasgupta, J. K. Mondal, and P. Dutta, Optimized Video Steganography Using Genetic Algorithm (GA), Procedia Technol. 10, 131–137 (2013). [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). [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). [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). [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). [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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