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On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

Received: 19 December 2017     Accepted: 2 January 2018     Published: 19 January 2018
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Abstract

Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.

Published in International Journal of Medical Imaging (Volume 5, Issue 6)
DOI 10.11648/j.ijmi.20170506.12
Page(s) 70-78
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Feature Selection, Multi-Fractal Descriptor, Classification Accuracy, Naïve Bayes, Bagged Decision Tree, Emphysema Patterns

References
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Cite This Article
  • APA Style

    Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. (2018). On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. International Journal of Medical Imaging, 5(6), 70-78. https://doi.org/10.11648/j.ijmi.20170506.12

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    ACS Style

    Musibau Adekunle Ibrahim; Oladotun Ayotunde Ojo; Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int. J. Med. Imaging 2018, 5(6), 70-78. doi: 10.11648/j.ijmi.20170506.12

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    AMA Style

    Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye. On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images. Int J Med Imaging. 2018;5(6):70-78. doi: 10.11648/j.ijmi.20170506.12

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  • @article{10.11648/j.ijmi.20170506.12,
      author = {Musibau Adekunle Ibrahim and Oladotun Ayotunde Ojo and Peter Adefioye Oluwafisoye},
      title = {On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images},
      journal = {International Journal of Medical Imaging},
      volume = {5},
      number = {6},
      pages = {70-78},
      doi = {10.11648/j.ijmi.20170506.12},
      url = {https://doi.org/10.11648/j.ijmi.20170506.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20170506.12},
      abstract = {Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images
    AU  - Musibau Adekunle Ibrahim
    AU  - Oladotun Ayotunde Ojo
    AU  - Peter Adefioye Oluwafisoye
    Y1  - 2018/01/19
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    N1  - https://doi.org/10.11648/j.ijmi.20170506.12
    DO  - 10.11648/j.ijmi.20170506.12
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
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    EP  - 78
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20170506.12
    AB  - Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.
    VL  - 5
    IS  - 6
    ER  - 

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Author Information
  • Department of Information and Communication Technology, Osun State University, Osogbo, Nigeria

  • Department of Physics, Osun State University, Osogbo, Nigeria

  • Department of Physics, Osun State University, Osogbo, Nigeria

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