A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

  • R. RathiEmail author
  • D. P. AcharjyaEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 127)


Internet made a big revolution in the real world and thus poses so many challenges to the researchers by generating an enormous amount of data. The data generated contains an enormous amount of unwanted information. Before processing with such a dataset, the important features present in the dataset must be retrieved. The feature selection process is important because the performance of a model built for the purpose of classification, prediction or clustering depends mainly on the number of relevant features present in the dataset. In this proposed work, the real coded genetic algorithm is used to find the important features by considering the fuzzy rough degree of dependency as its fitness function for finding out optimum features for agricultural dataset, iris dataset and Pima Indian diabetes dataset. The experimental results show that the proposed work produces relevant features by maintaining classification accuracy.


Real coded genetic algorithm Fuzzy rough Crossover Mutation 


  1. 1.
    Pawlak Z (2012) Rough sets: theoretical aspects of reasoning about data, vol 9. Springer Science & Business Media, BerlinGoogle Scholar
  2. 2.
    Chakraborty G, Chakraborty B (2004) A rough-GA hybrid algorithm for rule extraction from large data. In: IEEE International conference on computational intelligence for measurement systems and applications (CIMSA 2004), pp 85–90Google Scholar
  3. 3.
    Jensen R, Shen Q (2005) Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Syst 149(1):5–20MathSciNetCrossRefGoogle Scholar
  4. 4.
    Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89CrossRefGoogle Scholar
  5. 5.
    Jensen R, Shen Q (2009) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 17(4):824–838CrossRefGoogle Scholar
  6. 6.
    Bhatt RB, Gopal M (2005) On fuzzy-rough sets approach to feature selection. Patt Recogn Lett 26(7):965–975CrossRefGoogle Scholar
  7. 7.
    Qian Y, Wang Q, Cheng H, Liang J, Dang C (2015) Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst 258:61–78MathSciNetCrossRefGoogle Scholar
  8. 8.
    Anaraki JR, Samet S, Banzhaf W, Eftekhari M (2016) A new fuzzy-rough hybrid merit to feature selection. Trans Rough Sets XX:1–23Google Scholar
  9. 9.
    Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Patt Anal Mach Intell 26(11):1424–1437CrossRefGoogle Scholar
  10. 10.
    Jung M, Zscheischler J (2013) A guided hybrid genetic algorithm for feature selection with expensive cost functions. Procedia Comput Sci 18:2337–2346CrossRefGoogle Scholar
  11. 11.
    Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception. Academic, London, pp 65–93Google Scholar
  12. 12.
    Acharjya DP, Roy D, Rahaman MA (2012) Prediction of missing associations using rough computing and Bayesian classification. Int J Intell Syst Appl 4(11):1–13Google Scholar
  13. 13.
    Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  14. 14.
    Hssina B, Merbouha A, Ezzikouri H, Erritali M (2014) A comparative study of decision tree ID3 and C4. 5. Int J Adv Comput Sci Appl 4(2):13–19Google Scholar
  15. 15.
    Goldberg DE (2006) Genetic algorithms. Pearson Education India, New DelhiGoogle Scholar
  16. 16.
    Rathi R, Acharjya DP (2017) A rule based classification for agriculture vegetable production for Tiruvannamalai District using rough set and genetic algorithm. Int J Fuzzy Syst ApplGoogle Scholar
  17. 17.
    Rathi R, Acharjya DP (2018) A framework for prediction using rough set and real coded genetic algorithm. Arab J Sci Eng 43(8):4215–4227CrossRefGoogle Scholar
  18. 18.
    Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learningGoogle Scholar
  19. 19.
    Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Seventh international conference on tools with artificial intelligence. IEEE, pp 388–391Google Scholar
  20. 20.
    Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, Berlin, pp 171–182Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.VIT UniversityVelloreIndia

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