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Empirical Analysis of Supervised and Unsupervised Filter based Feature Selection Methods for Breast Cancer Classification from Digital Mammograms

by Subodh Srivastava, Neeraj Sharma, S. K. Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 8
Year of Publication: 2014
Authors: Subodh Srivastava, Neeraj Sharma, S. K. Singh
10.5120/15373-3935

Subodh Srivastava, Neeraj Sharma, S. K. Singh . Empirical Analysis of Supervised and Unsupervised Filter based Feature Selection Methods for Breast Cancer Classification from Digital Mammograms. International Journal of Computer Applications. 88, 8 ( February 2014), 20-33. DOI=10.5120/15373-3935

@article{ 10.5120/15373-3935,
author = { Subodh Srivastava, Neeraj Sharma, S. K. Singh },
title = { Empirical Analysis of Supervised and Unsupervised Filter based Feature Selection Methods for Breast Cancer Classification from Digital Mammograms },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 8 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number8/15373-3935/ },
doi = { 10.5120/15373-3935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:06.465373+05:30
%A Subodh Srivastava
%A Neeraj Sharma
%A S. K. Singh
%T Empirical Analysis of Supervised and Unsupervised Filter based Feature Selection Methods for Breast Cancer Classification from Digital Mammograms
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 8
%P 20-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the design and development of an automated CAD tool for breast cancer detection and diagnosis, the various steps include enhancement, segmentation, feature extraction, feature selection and classification. The feature selection plays an important role in the design of the said CAD tool as it aims towards the redundant feature elimination and relevant feature selection. The selected feature set also decides the efficacy of the chosen classifier for classification of mammograms. In literature, various filter based feature selection methods exists under unsupervised and supervised categories based on different basis criterion. The filter based feature selection methods ranks the extracted feature sets based on some criteria in descending order of their importance. The various methods produce different feature subsets which are associated with different performance measures. In this paper, an evaluation and comparative study of various unsupervised and supervised feature selection methods are presented for breast cancer classification from digital mammograms though various classifiers. The study aims towards finding out the better feature selection method and associated classifier which gives better performance.

References
  1. Cancer facts and figures 2011. http://www. cancer. org/Cancer/BreastCancer /DetailedGuide/ breast-cancer-key-statistics.
  2. American College of Radiology (ACR) (2003): ACR Breast Imaging Reporting and Data System, Breast Imaging Atlas, 4th Edn. , Reston, VA, USA.
  3. Subashini T. S. , Ramalingam V. , Palanivel S. 2010. Automated assessment of breast tissue density in digital mammograms. Computer Vision and Image Understanding. 114, 33–43.
  4. Rangayyan, R. M. , Ayres, F. J. , Desautels, J. E. L 2007. A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs. Journal of the Franklin Institute. 312–348.
  5. Bozek J. , Mustra M. , Delac K. , and Grgic M. 2009. A Survey of Image Processing Algorithms in Digital Mammography. Rec. Advances in Mult. Sig. Process. And Commn. (Grgic M. Eds. ) SCI 231. Springer-Verlag Berlin Heidelberg. 631-656.
  6. Tang J. , Rangayyan R. , Xu J. , Naqa I. El, Yang Y. 2009. Computer-aided detection and diagnosis of breast cancer with mammography. IEEE Transactions Recent advances, Information Technology in Biomedicine, 13(2), 236–251.
  7. Srivastava S. , Sharma N. , Singh S. K. 2013. Image Analysis and Understanding Techniques for Breast Cancer Detection from Digital Mammograms. In Research developments in image processing and computer vision, Srivastava R. Singh S. K. , and Shukla K. K. (Eds. ), Chapter 8, pp. 123-148. IGI Global, USA.
  8. Duda R. O. , Hart P. E. and Stork D. G. 2000. Pattern Classification (2nd Edition). Wiley, India.
  9. Guyon I. , and Elisseeff A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157–1182.
  10. Kohavi R. and John G. H. 1997. Wrapper for feature subset selection. Artificial Intelligence. 97(1-2), 273–324. .
  11. Marko R. S. , Igor K. 2003. Theoretical and empirical analysis of Relief and ReliefF. Machine Learning Journal. 53, 23-69.
  12. Peng H. , Long F. , and Ding C. 2005. Feature selection based on mutual information: criteria of max-dependency, max- relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27( 8), 1226-1238.
  13. Yu L. , and Liu H. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In proceedings of the ICML'03 conference. 856-863.
  14. Gilad-Bachrach R. , Navot A. , and Tishby N. 2004. Margin based feature selection - theory and algorithms. In Proceedings of 21st ICML conference. 337–344.
  15. Furey T. S. , Cristianini N. , Duffy N. , Bednarski D. W. , Schummer M. , and Haussler D. 2000. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics. 16(10), 906-914.
  16. He, X. , Cai, D. , Niyogi, P. 2005. Laplacian score for feature selection. In: Advances in Neural Information Processing System, vol. 17. MIT press, Cambridge.
  17. Yang F. and Mao K. Z. 2011. Robust Feature Selection for Microarray Data Based on Multicriterion Fusion. IEEE/ACM Transact. On Comput. Biology and Bioinformatics. 8( 4), 1080-1092.
  18. Zuiderveld, K. (1994) 'Contrast Limited Adaptive Histogram Equalization', Graphic Gems IV. San Diego: Academic Press Professional, pp. 474–485.
  19. Jain, A. K. 2006. Fundamentals of Digital Image Processing. PHI, India.
  20. Cai, W. , Chen, S. and Zhang, D. 2007. Fast and robust fuzzy c-means clustering algorithm incorporating local information for image segmentation. Pattern Recognition. 40( 3), 825-838.
  21. Srivastava, S. , Sharma, N. , Singh, S. K. and Srivastava, R. 2013. Design, analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammograms. Int. J. Biomedical Engineering and Technology. 13( 3), 270–300.
  22. Haralick, R. M. , Shanmugam, K. and Dinstein, I. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. SMC-3 (6), 610-621.
  23. Gonzalez, R. C. 2003. Digital Image Processing. Prentice Hall, India.
  24. Huang, K. and Aviyente, S. 2008. Wavelet Feature Selection for Image Classification. IEEE Transactions on Image Processing, 17( 9),1709-1720.
  25. Grigorescu, S. E. , Petkov, N. and Kruizinga, P. 2002. Comparison of Texture Features Based on Gabor Filters. IEEE Transactions on Image Processing. 11(10),1160-1167.
  26. V. Vapnik 1998. Statistical Learning Theory. John Wiley & Sons, USA.
  27. Mammographic Image Analysis Society (MIAS) database. 2013. http://www. mammoimage. org/databases/
Index Terms

Computer Science
Information Sciences

Keywords

Supervised feature selection methods unsupervised feature selection methods comparative study classifier selection CAD tool breast cancer detection MIAS database.

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