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Original Articles

GA-Based Feature Selection Method for Imbalanced Data with Application in Radio Signal Recognition

, , &
Pages 39-47 | Received 08 Feb 2015, Accepted 27 Oct 2015, Published online: 14 Dec 2015

References

  • Q. H. Zhu, Radio monitoring and communication investigation, Beijing: people's posts and telecommunications publishing house, 2005. (In Chinese)
  • D. Rivero, E. Femandez-Blanco, J. Dorado, et al. A New Signal Classification Technique by Means of Genetic Algorithms and kNN, IEEE Conf. on Evolutionary Computation (CEC), Coruna, Spain, 4(2011), pp. 581–586.
  • A. Ebrahimzadeh, R. Ghazalian, Blind digital modulation classification in software radio using the optimized classifier and feature subset selection, Engineering Applications of Artificial Intelligence, 24(2011), pp. 50–59. doi: 10.1016/j.engappai.2010.08.008
  • M. Chen, Q. Zhu, Cooperative automatic modulation recognition in cognitive radio, The Journal of China Universities of Posts and Telecommunications , 17(2)(2010), pp. 46–52. doi: 10.1016/S1005-8885(09)60445-3
  • J. L. Yang, Z. Pei, L. Zou,et al. Feature extraction of radio signals using attribute reduction of formal concepts, International Journal of Innovative Computing, Information and Control, 7(6)(2011), pp. 3331–3343.
  • Z. H. Zhang, F. L. Ma, Z. Pei, Recognition of Aviation Interference Signal Based on K-means Clustering Algorithm, The national conference on radio application and management, 11(2013), pp. 106–111. (In Chinese)
  • F. Provost, T. Fawcett, Robust classification for imprecise environments, Machine Learning, 42(3)(2001), pp. 203–231. doi: 10.1023/A:1007601015854
  • N. Chawla, K. Bowyer, L. Hall, et al. SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence Research, 16 (2002), pp. 321–357.
  • M. Galar, A. Fernandez, E. Barrenechea, et al. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 99 (2011), pp. 1–22.
  • G. X. He, H. Han, W.Y. Wang, An over-sampling expert system for learning from imbalanced data sets, International Conference on Neural Networks and Brain, (2005), pp. 537–541.
  • H. Han, W. Y. Wang, B. H. Mao, Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, International Conference on Intelligent Computing, (2005), pp. 878–887.
  • R. Barandela, R. M. Valdovinos, J. S. Sánchez et al. The imbalanced training sample problem: under or over sampling, Proc of International Workshops on Structural Syntactic and Statistical Pattern Recognition, (2004), pp. 806–814. doi: 10.1007/978-3-540-27868-9_88
  • L.M. Manevitz, M. Yousef, One-class SVMs for document classification, Journal of Machine Learning Research, 2 (2001), pp. 139–154.
  • H. J. Lee, S. Cho, The novelty detection approach for difference degrees of class imbalance, Lecture Notes in Computer Science, 4233 (2006), pp. 21–30.
  • G. Wu, Y. C. Edward, KBA: Kernel boundary alignment considering imbalanced data distribution, The IEEE transactions on knowledge and data engineering, 17(6)(2005), pp. 786–795. doi: 10.1109/TKDE.2005.95
  • T. Imam, K. M. Ting, J. Kamruzzaman, z-SVM: An SVM for improved classification of imbalanced data, Australian Joint Conference on AI, (2006), pp. 264–273.
  • H. J. Lee, S. Z. Cho, Focusing on non-respondents: Response modeling with novelty detectors, Expert Systems with Applications, 33(2), (2007), pp. 522–530. doi: 10.1016/j.eswa.2006.05.016
  • Z. Zheng, X. Wu, R. Srihari, Feature selection for text categorization on imbalanced data, ACM SIGKDD Explor. Newslett. (Special Issue on Learning from Imbalanced Datasets), 6 (1) (2004), pp. 80–89. doi: 10.1145/1007730.1007741
  • M. Wasikowski, X. W. Chen, Combating the small sample class imbalance problem using feature selection, IEEE Transactions on Knowledge and Data Engineering, 22(10)(2010), pp. 1388–1400. doi: 10.1109/TKDE.2009.187
  • R. Wang, K. Tang, Feature Selection for MAUC Oriented Classification Systems, Neurocomputing, 89(2012), pp. 39–54. doi: 10.1016/j.neucom.2012.01.013
  • H. Frohlich, O. Chapelle, Feature selection for support vector machines by means of genetic algorithms, Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, CA, USA, (2003), pp. 142–148.
  • C. L. Huang, C. J. Wang, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, 31(2006), pp. 231–240. doi: 10.1016/j.eswa.2005.09.024
  • Y. N. Liu, G. Wang, X. D. Zhu, et al. Feature selection based on adaptive multi-population genetic algorithm, Journal of Jilin University(Engineering and Technology Edition), 41(6) (2011), pp. 1690–1693.
  • X. Zhou, Z. Pei, P. H. Liu, et al. A new method for feature selection of radio abnormal signal, ICIC Express Letters, 7(2)(2013), pp. 303–309.
  • Z. W. Ji, G. F. Wu, M. Hu, Feature Selection Based on Adaptive Genetic Algorithm and SVM, Computer Engineering, 35(14) (2011), pp. 200–202.
  • O. Soufan, D. Kleftogiannis, P. Kalnis, et al. DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm, PloS one, 10(2) (2015), pp. 1–23.
  • L. M. Du, Y. Xu, L. Q. Jin, Feature Selection for Imbalanced Datasets Based on Improved Genetic Algorithm, Proc of the 11th International FLINS Conference on Decision Making and Soft Computing, Brazil, (2014), pp.119–124.
  • Z. Y. Lin, Z. F. Hao, X. W. Yang, Effects of Several of South China University of Technology (Natural Science Edition), 38(4)(2010), pp. 147–155.
  • A. Asuncion, D. Newman. UCI repository of machine learning databases [DB/OL]. [2009-04-03]. http://www.Ics.uci.edu/~mlearn/MLRep-ository.Html.
  • J. Q. Han, L. Zhang, R. Tie, Speech Signal Processing, Beijing: Tsinghua University Press, (2004). (In Chinese)
  • H.B. He and Edwardo A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21(9) (2009), pp. 1263–1284. doi: 10.1109/TKDE.2008.239

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