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

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Advances in Distributed Computing and Machine Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 127))

Abstract

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.

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References

  1. Pawlak Z (2012) Rough sets: theoretical aspects of reasoning about data, vol 9. Springer Science & Business Media, Berlin

    Google Scholar 

  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 85–90

    Google Scholar 

  3. Jensen R, Shen Q (2005) Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Syst 149(1):5–20

    Article  MathSciNet  Google Scholar 

  4. Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Bhatt RB, Gopal M (2005) On fuzzy-rough sets approach to feature selection. Patt Recogn Lett 26(7):965–975

    Article  Google Scholar 

  7. Qian Y, Wang Q, Cheng H, Liang J, Dang C (2015) Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst 258:61–78

    Article  MathSciNet  Google Scholar 

  8. Anaraki JR, Samet S, Banzhaf W, Eftekhari M (2016) A new fuzzy-rough hybrid merit to feature selection. Trans Rough Sets XX:1–23

    Google Scholar 

  9. Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Patt Anal Mach Intell 26(11):1424–1437

    Article  Google Scholar 

  10. Jung M, Zscheischler J (2013) A guided hybrid genetic algorithm for feature selection with expensive cost functions. Procedia Comput Sci 18:2337–2346

    Article  Google Scholar 

  11. Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception. Academic, London, pp 65–93

    Google Scholar 

  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):1–13

    Google Scholar 

  13. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  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):13–19

    Google Scholar 

  15. Goldberg DE (2006) Genetic algorithms. Pearson Education India, New Delhi

    Google Scholar 

  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 Appl

    Google Scholar 

  17. Rathi R, Acharjya DP (2018) A framework for prediction using rough set and real coded genetic algorithm. Arab J Sci Eng 43(8):4215–4227

    Article  Google Scholar 

  18. Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning

    Google Scholar 

  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 388–391

    Google Scholar 

  20. Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, Berlin, pp 171–182

    Google Scholar 

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Author information

Authors and Affiliations

  1. VIT University, Vellore, India

    R. Rathi & D. P. Acharjya

Authors
  1. R. Rathi
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  2. D. P. Acharjya
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Corresponding authors

Correspondence to R. Rathi or D. P. Acharjya .

Editor information

Editors and Affiliations

  1. Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Asis Kumar Tripathy

  2. San Diego State University, San Diego, CA, USA

    Mahasweta Sarkar

  3. Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar, Odisha, India

    Jyoti Prakash Sahoo

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. National Institute of Technology, Rourkela, Rourkela, Odisha, India

    Suchismita Chinara

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

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Rathi, R., Acharjya, D.P. (2021). A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_48

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