Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms

Expert Systems with Applications

Volumn 41, Issue 4 PART 1, 2014, Pages 1476-1482

  Zheng, Bichen ^a   Yoon, Sang Won ^a   Lam, Sarah S ^a  

^a State University of New York at Binghamton   (United States)

Author keywords

Cancer diagnosis; Data mining; K means; Support vector machine

Indexed keywords

BENIGN AND MALIGNANT TUMORS; BREAST CANCER DIAGNOSIS; CANCER DIAGNOSIS; FEATURE EXTRACTION AND SELECTION; K-MEANS; MACHINE LEARNING REPOSITORY; SUPPORT VECTOR MACHINE ALGORITHM; UNIVERSITY OF CALIFORNIA;

ALGORITHMS; DATA MINING; DISEASES; FEATURE EXTRACTION; INFORMATION FILTERING; SUPPORT VECTOR MACHINES; TUMORS;

DIAGNOSIS;

EID: 84888306351     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.08.044     Document Type: Article

References (28)

1. M.F. Akay Support vector machines combined with feature selection for breast cancer diagnosis Expert Systems with Applications 36 2009 3240 3247
2. K.P. Bennett, and J.A. Blue A support vector machine approach to decision trees Proceedings of IEEE world congress on computational intelligence 1998 IEE Anchorage, AK 2396 2401
3. Bradley, P. S., Bennett, K. P., & Demiriz, A. (2000). Constrained K-means clustering. Technical report microsoft research redmond. WA, USA.
4. M.S. Chen, J. Han, and P.S. Yu Data mining: An overview from a database perspective IEEE Tra (Pubitemid 126774144)
5. Y.-W. Chen, and C.-J. Lin Combining svms with various feature selection strategies Feature extraction 2006 Springer Berlin Heidelberg 315 324
6. K. Chidananda Gowda, and E. Diday Symbolic clustering using a new dissimilarity measure Pattern Recognition 24 1991 567 578 (Pubitemid 21650079)
7. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 1995 273 297
8. I.S. Dhillon, S. Mallela, and R. Kumar A divisive information theoretic feature clustering algorithm for text classification The Journal of Machine Learning Research 3 2003 1265 1287
9. J.G. Elmore, C.K. Wells, C.H. Lee, D.H. Howard, and A.R. Feinstein Variability in radiologists interpretations of mammograms New England Journal of Medicine 331 1994 1493 1499
10. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth From data mining to knowledge discovery in databases Artificial Intelligence Magazine 17 1996 37 54
11. T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, and D. Haussler Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16 2000 906 914
12. C.-L. Huang, H.-C. Liao, and M.-C. Chen Prediction model building and feature selection with support vector machines in breast cancer diagnosis Expert Systems with Applications 34 2008 578 587 (Pubitemid 46756285)
13. S. Idicula-Thomas, A.J. Kulkarni, B.D. Kulkarni, V.K. Jayaraman, and P.V. Balaji A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in escherichia coli Bioinformatics 22 2006 278 284 (Pubitemid 43205372)
14. A. Jain, and D. Zongker Feature selection: Evaluation, application, and small sample performance IEEE Transactions on Pattern Analysis and Machine Intelligence 19 1997 153 158 (Pubitemid 127828334)
15. A.K. Jain Data clustering: 50 Years beyond k-means Pattern Recognition Letters 31 2010 651 666
16. A.K. Jain, M.N. Murty, and P.J. Flynn Data clustering: A review ACM Computing Surveys (CSUR) 31 1999 264 323
17. Mu, T., & Nandi, A. K. (2008). Breast cancer diagnosis from fine-needle aspiration using supervised compact hyperspheres and establishment of confidence of malignancy. The 16th European Signal Processing Conference, EUSIPCO, Lausanne, Switzerland.
18. Nezafat, R., Tabesh, A., Akhavan, S., Lucas, C., & Zia, M. (1998). Feature selection and classification for diagnosing breast cancer. In Proceedings of international association of science and technology for development international conference (pp. 310-313).
19. C.A. Pena-Reyes, and M. Sipper A fuzzy-genetic approach to breast cancer diagnosis Artificial Intelligence in Medicine 17 1999 131 155 (Pubitemid 29474991)
20. J. Platt Sequential minimal optimization: A fast algorithm for training support vector machines Advances In Kernel Methods - Support Vector Learning 1998 MIT Press Cambridge, MA, USA 185 208
21. K. Polat, and S. Güneş Breast cancer diagnosis using least square support vector machine Digital Signal Processing 17 2007 694 701 (Pubitemid 46819053)
22. Prasad, Y., Biswas, K., & Jain, C. (2010). Svm classifier based feature selection using ga, aco and pso for sirna design. In Proceedings of the first international conference on advances in swarm intelligence (pp. 307-314).
23. W. Siedlecki, and J. Sklansky A note on genetic algorithms for large-scale feature selection Pattern Recognition Letters 10 1989 335 347
24. R. Siegel, D. Naishadham, and A. Jemal Cancer statistics, 2012 CA: A Cancer Journal for Clinicians 62 2012 10 29
25. M. Venkatadri, and C.R. Lokanatha A review on data mining from past to the future International Journal of Computer Applications 15 2011 19 22
26. X.-Y. Wang, X.-J. Zhang, H.-Y. Yang, and J. Bu A pixel-based color image segmentation using support vector machine and fuzzy c-means Neural Networks 33 2012 148 159
27. W.H. Wolberg, W.N. Street, and O.L. Mangasarian Image analysis and machine learning applied to breast cancer diagnosis and prognosis Analytical and Quantitative Cytology and Histology 17 1995 77 87
28. R. Xu, and D. Wunsch Survey of clustering algorithms IEEE Transactions on Neural Networks 16 2005 645 678 (Pubitemid 40718010)