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Kakaei, H., Mollazadeh, M., Teymour Pour, B. (2015). An Optimized Unsupervised Feature Selection Algorithm. Electronic and Cyber Defense, 3(3), 1-7.
Hamid Reza Kakaei; Mahdi Mollazadeh; Babak Teymour Pour. "An Optimized Unsupervised Feature Selection Algorithm". Electronic and Cyber Defense, 3, 3, 2015, 1-7.
Kakaei, H., Mollazadeh, M., Teymour Pour, B. (2015). 'An Optimized Unsupervised Feature Selection Algorithm', Electronic and Cyber Defense, 3(3), pp. 1-7.
Kakaei, H., Mollazadeh, M., Teymour Pour, B. An Optimized Unsupervised Feature Selection Algorithm. Electronic and Cyber Defense, 2015; 3(3): 1-7.

An Optimized Unsupervised Feature Selection Algorithm

پدافند الکترونیکی و سایبری
Article 2, Volume 3, Issue 3, February 2020, Pages 1-7    PDF (824.28 K)
Authors
Hamid Reza Kakaei1; Mahdi Mollazadeh* 1; Babak Teymour Pour2
1Instructor, Imam Hossein University, Tehran, Iran
2Assistant Professor, Tarbiat Modares University, Tehran, Iran
Receive Date: 31 July 2013,  Revise Date: 21 June 2023,  Accept Date: 19 September 2018 
Abstract
Choosing a feature vector for maximizing the success of a classifier machine is very effective. In
thispaper, using a combination of different methods to calculate the core function, an unsupervised feature
selection algorithm improvement has been proposed. Feature vector obtained by the proposed algorithm,
will maximizes output accuracy of backpropagation neural network classifier. In this paper we used case
study of standard encoding of images compressed by alternate method and uncompressed images
classifying based on their relative bit stream. Standards for classifications are JPEG and JPEG2000 and
for uncompressed images is TIFF format. Using this feature vector obtained by the proposed algorithm,
classifier accuracy will be about 98%.
Keywords
Feature Vector; Feature Vector Selection; Neural Network; Classification; Image Compressing Standard
References

[1]     P. Suresh, R. M. D. Sundaram, and A.  Arumugam,    “Feature Extraction in Compressed Domain for Content Based Image Retrieval,” International Conference on Advanced Computer Theory and Engineering, Phuket, 2008.

[2]   N. Verma, S. S. Khan, and S. Kant, “Statistical Feature Extraction to Discriminate Various Languages: Plain and Crypt,” Scientific Analysis Group, 2003.

[3]     W. H. Vellerling, W. T. Teukolsky, and S. A. Flannery,  “Numerical Recipes in C,” Second Edition, 1995.

[4]     G. Zhang, “Neural networks for classification: a survey,” IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, vol. 30, no 4, pp. 451-462, 2000.

[5]     I. H. Witten and E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations,” Morgan Kaufmann Publishers, 2000.

[6]     H. Liu and Z. Zhao, “Spectral Feature Selection for Supervised and Unsupervised Learning,” Proceedings of the 24th International Conference on Machine Learning, 2007.

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