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Fuzzy Rough Set-Based Feature Selection with Improved Seed Population in PSO and IDS

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Computational Intelligence: Theories, Applications and Future Directions - Volume II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 799))

Abstract

In this paper, fuzzy lower approximation-based fuzzy rough set is used for selection of features. A distributed sampling (DS)-based initialization method is introduced to pick better seed population, in particle swarm optimization (PSO) and intelligent dynamic swarm (IDS). PSO and IDS are used for simultaneously selecting the appropriate feature subset. Fitness function for these computations is fuzzy rough dependency measure. Using the proposed initialization, while using PSO and IDS, improvement in size of selected subset of features with improved classification accuracy is also demonstrated.

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References

  1. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  2. Ren, D., Ma, A.Y.: Research on feature extraction from remote sensing image. In: International Conference in Computer Application and System Modeling, vol. 1, pp. V144–V148 (2010)

    Google Scholar 

  3. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Article  Google Scholar 

  4. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognit. Lett. 28(4), 459–471 (2007)

    Article  Google Scholar 

  5. Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)

    Article  Google Scholar 

  6. Bae, C., Yeh, W.-C., Chung, Y.Y., Liu, S.-L.: Feature selection with intelligent dynamic swarm and rough set. Expert Syst. Appl. 37(10), 7026–7032 (2010)

    Article  Google Scholar 

  7. Verma, N.K., Maini, T., Salour, A.: Acoustic signature based intelligent health monitoring of air compressors with selected features. In: 2012 Ninth International Conference on Information Technology: New Generations (ITNG), pp. 839–845. IEEE (2012)

    Google Scholar 

  8. Maini, T., Misra, R.K., Singh, D.: Optimal feature selection using elitist genetic algorithm. In: IEEE Workshop on Computational Intelligence: Theories, p. 2015. Applications and Future Directions (WCI), IEEE (2015)

    Google Scholar 

  9. Maini, T., Kumar, A., Misra, R.K., Singh, D.: Feature selection with intelligent dynamic swarm and fuzzy rough set. In: IEEE International Conference on Computing, Communication and Automation 2017 (ICCCA 2017)

    Google Scholar 

  10. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Google Scholar 

  11. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognit. Lett. 24(6), 833–849 (2003)

    Article  Google Scholar 

  12. Mac ParthaláIn, N., Jensen, R.: Unsupervised fuzzy-rough set-based dimensionality reduction. Inf. Sci. 229, 106–121 (2013)

    Google Scholar 

  13. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets*. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)

    Article  Google Scholar 

  14. Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., Lin, Y.: A fitting model for feature selection with fuzzy rough sets. IEEE Trans. Fuzzy Syst. (2016)

    Google Scholar 

  15. Mac Parthaláin, N., Jensen, R.: Fuzzy-rough feature selection using flock of starlings optimisation. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2015)

    Google Scholar 

  16. Eberhart, R.C., Kennedy, J. et al.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science

    Google Scholar 

  17. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)

    Google Scholar 

  18. Shi, Y. et al.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81–86. IEEE (2001)

    Google Scholar 

  19. Lichman, M.: UCI machine learning repository (2013) [Online]. http://archive.ics.uci.edu/ml

  20. Quinlan, J.R.: C4.5: Programming for Machine Learning. Morgan Kauffmann (1993)

    Google Scholar 

  21. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  22. Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995)

    Google Scholar 

  23. Witten, I., Frank, E.: Data mining: practical machine learning tools with java implementations. In: Kaufmann, M. (ed.) San Francisco (2000)

    Google Scholar 

  24. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    Article  Google Scholar 

  25. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14(2), 1137–1145 (1995)

    Google Scholar 

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Authors and Affiliations

  1. IIT(BHU), Varaansi, Varanasi, 221005, Uttar Pradesh, India

    Tarun Maini, Abhishek Kumar, Rakesh Kumar Misra & Devender Singh

Authors
  1. Tarun Maini
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  2. Abhishek Kumar
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  3. Rakesh Kumar Misra
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  4. Devender Singh
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Corresponding author

Correspondence to Abhishek Kumar .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Maini, T., Kumar, A., Misra, R.K., Singh, D. (2019). Fuzzy Rough Set-Based Feature Selection with Improved Seed Population in PSO and IDS. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_11

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