GA-Based Feature Selection for Squid’s Classification

  • K. HimabinduEmail author
  • S. Jyothi
  • D. M. Mamatha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


In this work, twenty features are extracted from Squid species that is from their shape, color, and texture features. The extracted features are fin width, fin length, head length, head width, mantle length, mantle width, total length, contrast, correlation, homogeneity, entropy, R mean, R standard deviation, R skewness, G mean, G standard deviation, G skewness, B mean, B standard deviation, B skewness. These too many extracted features may contain a lot of redundancy, increases the time complexity, and hence automatically degrade the accuracy. Hence, we adopted genetic algorithm for feature selection. Feature selection enhances the performance of concerned classifiers. Selected features using GA are validated with fuzzy system (FS), and it gives the better accuracy.


Squid species Feature selection Genetic algorithm Fuzzy system Species classification 



This work is carried out under DBT-MRP, New Delhi.


  1. 1.
    Emam, W.M., Saad, A.A., Riad, R., et al.: Morphometric study and length—weight relationship on the squid Loligo forbesi (Cephalopoda: Loliginidae) from the Egyptian Mediterranean waters. Int. J. Environ. Sci. Eng. (IJESE) 5, 1–13 (2014)Google Scholar
  2. 2.
    Chakraborty, S.K., Biradar, R.S., Jaiswar, A.K.: Growth, mortality and population parameters of three cephalopod species, Loligo duvauceli (Orbigny), Sepia aculeata (Orbigny) and Sepiella inermis (Orbigny) from north-west coast of India. Indian J. Fish. 60(3), 1–7 (2013)Google Scholar
  3. 3.
    Hassan, R., Cohanim, B., Weck, O., et al.: A comparison of particle swarm optimization and genetic algorithm (2005)Google Scholar
  4. 4.
    Jianjiang, L., Zhao, T., Zhang, Y.: Feature selection based-on genetic algorithm for image annotation. Knowl.-Based Syst. 21, 887–891 (2008)CrossRefGoogle Scholar
  5. 5.
    Lu, H., Chen, J., Yan, K., et al.: A hybrid feature selection algorithm for gene expression data classification. Neurocomputing (2017), 2016.07.080 0925-2312/© 2017, ElsevierGoogle Scholar
  6. 6.
    Li, B., Lai, Y.K., Rosin, P.L.: Example-based image colorization via automatic feature selection and fusion. Neurocomputing 266, 687–698 (2017)CrossRefGoogle Scholar
  7. 7.
    Himabindu, K., Jyothi, S., Mamatha, D.M.: Squid species clustering based on color, shape and texture features. Int. J. Inf. Technol. (2017) [submitted paper waiting for further process]Google Scholar
  8. 8.
    Kumbhar, P., Mali, M.: A survey on feature selection techniques and classification algorithms for efficient text classification. Int. J. Sci. Res. (IJSR) 5(5) (2016). ISSN (Online) 2319-7064Google Scholar
  9. 9.
    Agrawal, N., Gonnade, S.: An approach for unsupervised feature selection using genetic algorithm. Int. J. Eng. Sci. Res. Technol. (2016). ISSN 2277-9655Google Scholar
  10. 10.
    Huang, C.-L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31, 231–240 (2006)CrossRefGoogle Scholar
  11. 11.
    Melanie, M.: An introduction to genetic algorithms. In: A Bradford Book. The MIT Press (1999)Google Scholar
  12. 12.
    Chatterjee, S., Bhattacherjee, A.: Genetic algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine. Eng. Appl. Artif. Intell. 24, 786–795 (2011)CrossRefGoogle Scholar
  13. 13.
    Zeng, D., Wang, S., Shen, Y., et al.: A GA based feature selection and parameter optimization for support tucker machine 111, 17–23 (2017)Google Scholar
  14. 14.
    Sangari Devi, S., Dhinakaran, S.: Crossover and mutation operations in GA-genetic algorithm. Int. J. Comput. Organ. Trends 3(4), 157–159 (2013). ISSN 2249-2593Google Scholar
  15. 15.
    Bhanu, B., Lin, Y.: Genetic algorithm based feature selection for target detection in SAR images. Elsevier Sci. Image Vis. Comput. 21, 591–608 (2003). Scholar
  16. 16.
    Khare, P., Burse, K.: Feature selection using genetic algorithm and classification using weka for ovari an cancer. Int. J. Comput. Sci. Inf. Technol. 7(1), 194–196 (2016). ISSN 0975-9646Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  2. 2.Department of SericultureSri Padmavati Mahila VisvavidyalayamTirupatiIndia

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