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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)

Abstract

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.

Keywords

Squid species Feature selection Genetic algorithm Fuzzy system Species classification 

Notes

Acknowledgements

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

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Copyright information

© 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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