An Optimized Unsupervised Feature Selection Algorithm

Authors

1 Instructor, Imam Hossein University, Tehran, Iran

2 Assistant Professor, Tarbiat Modares University, Tehran, Iran

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

References

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[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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History

  • Receive Date: 31 July 2013
  • Revise Date: 21 June 2023
  • Accept Date: 19 September 2018
  • Publish Date: 22 November 2015

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