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Feature Selection for Image Classification Based on a New Ranking Criterion

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DOI: 10.4236/jcc.2015.33013    3,092 Downloads   3,638 Views   Citations

ABSTRACT

In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief- SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Zhou, X. and Wang, J. (2015) Feature Selection for Image Classification Based on a New Ranking Criterion. Journal of Computer and Communications, 3, 74-79. doi: 10.4236/jcc.2015.33013.

References

[1] Guyon, I. and Elisseeff, A. (2003) An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1157-1182.
[2] Song, D.J. and Tao, D.C. (2010) Biologically Inspired Feature Manifold for Scene Classification, IEEE Transaction on Image Processing, 19, 174-184.
[3] Marko, R.S. and Lgor, K. (2003) Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53, 23-69.
[4] Zhang, Y., Ding, C. and Li, T. (2008) Gene Selection Algorithm by Combining ReliefF and mRMR. BMC Genomics, 9, S27. http://dx.doi.org/10.1186/1471-2164-9-S2-S27
[5] Suykens, J.A.K. and Vandewalle, J. (1999) Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9, 293-300. http://dx.doi.org/10.1023/A:1018628609742
[6] Guyon, I., Weston, J., Barnhill, S., et al. (2002) Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning, 46, 389-422. http://dx.doi.org/10.1023/A:1012487302797
[7] John, G.H., Kohavi, R., Pfleger, K. (1994) Irrelevant Features and the Subset Selection Problem. ICML, 94, 121-129.
[8] Yang, J., Yu, K., Gong, Y., et al. (2009) Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, 1794-1801.
[9] Van De Sande, K.E.A., Gevers, T. and Snoek, C.G.M. (2010) Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1582-1596. http://dx.doi.org/10.1109/TPAMI.2009.154
[10] Chun, Y.D., Kim, N.C. and Jang, I.H. (2008) Content-Based Image Retrieval Using Multiresolution Color and Texture Features. IEEE Transactions on Multimedia, 10, 1073-1084. http://dx.doi.org/10.1109/TMM.2008.2001357
[11] Carlin, M. (2001) Measuring the Performance of Shape Similarity Retrieval Methods. Computer Vision and Image Understanding, 84, 44-61. http://dx.doi.org/10.1006/cviu.2001.0935
[12] Mundra, P.A. and Rajapakse, J.C. (2010) SVM-RFE with MRMR Filter for Gene Selection. IEEE Transactions on NanoBioscience, 9, 31-37. http://dx.doi.org/10.1109/TNB.2009.2035284

  
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