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Fish Classification Based on Robust Features Selection Using Machine Learning Techniques

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Genetic and Evolutionary Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 387))

Abstract

The taxonomic identification of fishes is a time-consuming process and making errors is indispensable for those who are not specialists. This system proposes an automated species identification system to identify taxonomic characters of species based on specimens. It also provides statistical clues for assisting taxonomists to identify accurate species or review misdiagnosed species. For this system, feature selection is an essential step to effectively reduce data dimensionality. By using combination theory, this system creates the set of attribute pairs to construct the training dataset. And then each attribute pair in training dataset is tested by using two classifiers. Based on the accuracy result of each classifier on attribute pairs and the majority voting of each feature in these attribute pairs, this system selects the most relevant feature set. Finally, this system applied three supervised classifiers to verify the effectiveness of selected features subset.

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Author information

Authors and Affiliations

  1. University of Computer Studies, Mandalay, Myanmar (Burma)

    Than Thida Hnin & Khin Thidar Lynn

Authors
  1. Than Thida Hnin
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  2. Khin Thidar Lynn
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Corresponding author

Correspondence to Than Thida Hnin .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Hnin, T.T., Lynn, K.T. (2016). Fish Classification Based on Robust Features Selection Using Machine Learning Techniques. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-23204-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-23204-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23203-4

  • Online ISBN: 978-3-319-23204-1

  • eBook Packages: EngineeringEngineering (R0)

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