Soft Set Based Feature Selection Approach for Lung Cancer Images
Full Text(PDF, ) PP.1293-1299
| Author(s) |
|Jothi. G and Hannah Inbarani. H|
| KEYWORDS |
Clustering Techniques, CT Lung Cancer Images, Feature Extraction, Lung Segmentation, Soft Set Theory, Soft Set Based Quick Reduct, Unsupervised Feature Selection.
Lung cancer is the deadliest type of cancer for both men and women. Feature selection plays a vital role in cancer classification. This paper investigates the feature selection process in Computed Tomographic (CT) lung cancer images using soft set theory.
 J.L Ma, “The Peripheral Non-small Cell Lung Cancer Subtype Diagnosis
Model Research Based On Ct Image”Master Degree Thesis.
 Pabitra Mitra, C.A. Murthy and Sankar K. Pal, “Unsupervised Feature
Selection Using Feature Similarity”, IEEE Transactions On Pattern
Analysis And Machine Intelligence, vol 24, 2002.
 X. Fu, F .Tan, H.Wang, Y-Q.Zhang, R. Harrison, “Feature Similarity
Based Redundancy Reduction for Gene Selection”, Conference on Data
 Molodtsov, D.: “Soft set theory-first results”, Computers and Mathematics with Applications vol. 37, pp. 19–31, 1999.
 In-Hee Lee, Gerald H Lushington and Mahesh Visvanathan, “A filter-based feature selection approach for identifying potential biomarkers for
lung cancer”, Journal of Clinical Bioinformatics, 2011.
 Hanan M. Amer, Fatma E.Z. Abou-Chadi, and Marwa I. Obayya, “A
Computer-Aided System for Classifying Computed Tomographic (CT)
Lung Images Using Artificial Neural Network and Data Fusion”, International Journal of Computer Science and Network Security
(IJCSNS), vol.11, pp. 70-75, 2011.
 C.F. Aliferis, I. Tsamardinos, P.P. Massion, A. Statnikov, N.
Fananapazir, D. Hardin, “Machine Learning Models For Classification
Of Lung Cancer and Selection of Genomic Markers Using Array Gene
Expression Data”, American Association for Artificial Intelligence,
 Maji, P.K., Biswas, R., Roy, and A.R.: “Soft set theory. Computers and
Mathematics with Applications”, vol. 45, pp. 555–562, 2003.
 Tutut Herawan, Rozaida Ghazali, Mustafa Mat Deris, “Soft Set Theoretic Approach for Dimensionality Reduction”, International Journal of
Database Theory and Application, Vol. 3, No. 2, pp. 171–178, 2010.
 Norhalina Senan, Rosziati Ibrahim, Nazri Mohd Nawi, Iwan Tri Riyadi Yanto, and Tutut Herawan, “Soft Set Theory for Feature Selection
of Traditional Malay Musical Instrument Sounds”, ICICA 2010, LNCS
6377, pp. 253–260, 2010.
 J. Han and M. Kamber. “Data Mining: Concepts and Techniques”,
Morgan Kaufman, 2001.
 H. Liu and H. Motoda, editors. “Computational Methods of Feature
Selection”, Chapman and Hall/CRC Press, 2007.
 Herawan, T., Mustafa, M.D.: “On Multi-soft Sets Construction in Information Systems”, LNCS, Springer, vol. 5755, pp. 101–110, 2009.
 Xingbo Sun, Xiuhua Tang, Huanglin Zeng, and Shunyong Zhou, “A
Heuristic Algorithm Based on Attribute Importance for Feature Selection”, RSKT, pp. 189-196, 2008.
 Gonzales R.C. and Woods R.E., “Digital Image Processing”, 2nd Edition, New Jersey, Prentice Hall, 2004.
 David Jakobsson and Fredrik Olofsson, “Decision Support System for
Lung Cancer using PET/CT Images” ,Ph. D Thesis, 2004.
 R. M. Haralick, K. Shanmugan, I. Dinstein, “Textural features for image classification”, IEEE Trans. Syst., vol. 3, pp. 610–621, 1973.
 C. Velayutham and K. Thangavel, “Unsupervised Quick Reduct Algorithm Using Rough Set Theory”, Journal of Electronic Science And
Technology, vol. 9, pp. 163-168, 2011.
 Palaniappan, S., Hong, T.K, “Discretization of Continuous Valued Dimensions in OLAP Data Cubes”, International Journal of Computer
Science and Network Security vol. 8, pp. 116–126, 2008.