Volume 5, Issue 6, November 2017, Page: 70-78
On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images
Musibau Adekunle Ibrahim, Department of Information and Communication Technology, Osun State University, Osogbo, Nigeria
Oladotun Ayotunde Ojo, Department of Physics, Osun State University, Osogbo, Nigeria
Peter Adefioye Oluwafisoye, Department of Physics, Osun State University, Osogbo, Nigeria
Received: Dec. 19, 2017;       Accepted: Jan. 2, 2018;       Published: Jan. 19, 2018
DOI: 10.11648/j.ijmi.20170506.12      View  1547      Downloads  61
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.
Feature Selection, Multi-Fractal Descriptor, Classification Accuracy, Naïve Bayes, Bagged Decision Tree, Emphysema Patterns
To cite this article
Musibau Adekunle Ibrahim, Oladotun Ayotunde Ojo, Peter Adefioye Oluwafisoye, On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images, International Journal of Medical Imaging. Vol. 5, No. 6, 2017, pp. 70-78. doi: 10.11648/j.ijmi.20170506.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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