An Efficient Feature Selection Method Using Hybrid Particle Swarm Optimization with Genetic Algorithm

  • Arya NarayananEmail author
  • A. N. Praveen
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


The data mining applications over big data is a challenging task. The main issues of the big data are velocity problem, variety problem and the volume problem. We want to handle large amount of data in the case of big data such as medical data, sensor data, telephonic record data etc. In some cases, the classifier is not good enough and do not work well for data which have many features. Too many features are affects the effectiveness of classifier, some features may be redundant. Too many features goes through the classifier, which will cause increasing the workload of the classifier. In order to solve this problem, we need some optimized feature selection method. In this work proposed an algorithm called Hybrid Particle Swarm Optimization with Genetic Algorithm (HPSOGA). This is a very good feature selection method to find the optimal features for the classification to overcome the draw backs of the classification model. The efficiency of the classification model can be done using this feature selection algorithm through selecting the relevant and the significant features. So it help to obtain improved accuracy within the reasonable processing time of the classifier.


Feature selection Particle swarm optimization Genetic algorithm Big data analytics 


  1. 1.
    Tani, F.Y., Farid, D.M., Rahman, M.Z.: Ensemble of decision tree classifiers for mining web data streams. Int. J. Appl. Inf. Syst. 1, 30–36 (2012)Google Scholar
  2. 2.
    Akioka, S.: Task Graphs of Stream Mining Algorithms. (2013)Google Scholar
  3. 3.
    Yu, K.: Towards scalable and accurate online feature selection for big data. In: IEEE International Conference on Data Mining (2014)Google Scholar
  4. 4.
    Tekin, C., Canzian, L., Van Der Schaar, M.: Context-adaptive big data stream mining. In: Communication, Control, and Computing (Allerton) (2014)Google Scholar
  5. 5.
    Ruta, D.: Robust method of sparse feature selection for multi-label classification with Naive Bayes. In: Computer Science and Information Systems (FedCSIS) (2014)Google Scholar
  6. 6.
    Vu, A.T.: Distributed adaptive model rules for mining big data streams. In: 2014 IEEE International Conference on Big Data (2014)Google Scholar
  7. 7.
    Fong, S.: A scalable data stream mining methodology: stream-based holistic analytics and reasoning in parallel. In: 2nd International Symposium on Computational and Business Intelligence (2014)Google Scholar
  8. 8.
    Harde, S., Sahare, V.: ACO swarm search feature selection for data stream mining in big data. Int. J. Innov. Res. Comput. Commun. Eng. 3(12), 12087–12089 (2015)Google Scholar
  9. 9.
    Fong, S., Wong, R., Vasilakos, A.V.: Accelerated PSO swarm search feature selection for data stream mining big data. IEEE Trans. Serv. Comput. 9(1) (2016) Google Scholar
  10. 10.
    Wang, C.J.: A novel initialization method for particle swarm optimization-based FCM in big biomedical data. In: IEEE International Conference on Big Data (2015) Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyGovernment Engineering College IdukkiIdukkiIndia

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