A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA

Neural Computing and Applications

Volumn 23, Issue 3-4, 2013, Pages 719-728

  Uzer, Mustafa Serter ^a   Inan, Onur ^a   Yilmaz, Nihat ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Breast cancer diagnosis; Feature selection; Neural network; PCA; SBS; SFS

Indexed keywords

BREAST CANCER DIAGNOSIS; CROSS-VALIDATION METHODS; HYBRID FEATURE SELECTIONS; PCA; QUADRATIC DISCRIMINANT ANALYSIS; SBS; SEQUENTIAL FORWARD SELECTION; SFS;

ALGORITHMS; DIAGNOSIS; DISCRIMINANT ANALYSIS; DISEASES; HYBRID SYSTEMS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

FEATURE EXTRACTION;

EID: 84884596718     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-0982-6     Document Type: Article

References (34)

1. Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2): 3240-3247. doi: 10. 1016/j. eswa. 2008. 01. 009.
2. Organization WH (2011) Cancer http://www. who. int/cancer/en/. Accessed 5 Sept 2011.
3. Chou SM, Lee TS, Shao YE, Chen IF (2004) Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 27(1): 133-142. doi: 10. 1016/j. eswa. 2003. 12. 013.
4. Yeh WC, Chang WW, Chung YY (2009) A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst Appl 36(4): 8204-8211. doi: 10. 1016/j. eswa. 2008. 10. 004.
5. Goodman DE, Boggess L, Watkins A (2002) Artificial immune system classification of multiple-class problems. In: Proceedings of the Artificial Neural Networks in Engineering ANNIE 02, pp 179-183.
6. Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14): 2195-2207. doi: 10. 1016/S0167-8655(03)00047-3.
7. Polat K, Gunes S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17(4): 694-701. doi: 10. 1016/j. dsp. 2006. 10. 008.
8. Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36(2): 3465-3469. doi: 10. 1016/j. eswa. 2008. 02. 064.
9. Chen HL, Yang B, Liu J, Liu DY (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7): 9014-9022. doi: 10. 1016/j. eswa. 2011. 01. 120.
10. Marcano-Cedeno A, Quintanilla-Dominguez J, Andina D (2011) WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst Appl 38(8): 9573-9579. doi: 10. 1016/j. eswa. 2011. 01. 167.
11. Xu Y, Qi Z, Wang J (2011) Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput Appl. doi: 10. 1007/s00521-011-0547-0.
12. Senapati MR, Mohanty AK, Dash S, Dash PK (2011) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. doi: 10. 1007/s00521-011-0670-y.
13. Ubeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4): 1054-1062. doi: 10. 1016/j. eswa. 2006. 08. 005.
14. Wolberg WH (1991) Wisconsin Breast Cancer Database http://archive. ics. uci. edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin. names. Accessed 2 Sept 2011.
15. Sasikala M, Kumaravel N (2005) Comparison of feature selection techniques for detection of malignant tumor in brain images. In: Indicon 2005 Proceedings: 212-215.
16. Zhu ZX, Ong YS, Dash M (2007) Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern Part B Cybern 37(1): 70-76. doi: 10. 1109/Tsmcb. 2006. 883267.
17. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1-2): 273-324.
18. Bermejo P, Gamez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognit Lett 32(5): 701-711. doi: 10. 1016/j. patrec. 2010. 12. 016.
19. Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature-selection. Pattern Recognit Lett 15(11): 1119-1125.
20. Ladha L, Deepa T (2011) Feature selection methods and algorithms. Int J Comput Sci Eng (IJCSE) 3(5): 1787-1797.
21. Kim KS, Choi HH, Moon CS, Mun CW (2011) Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr Appl Phys 11(3): 740-745. doi: 10. 1016/j. cap. 2010. 11. 051.
22. Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York.
23. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7): 711-720.
24. Calisir D, Dogantekin E (2011) A new intelligent hepatitis diagnosis system: PCA-LSSVM. Expert Syst Appl 38(8): 10705-10708. doi: 10. 1016/j. eswa. 2011. 01. 014.
25. Sagiroglu S, Yilmaz N (2009) Web-based mobile robot platform for real-time exercises. Expert Syst Appl 36(2): 3153-3166. doi: 10. 1016/j. eswa. 2008. 01. 046.
26. Ubeyli ED, Guler I (2004) Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides. Neurocomputing 62: 349-365. doi: 10. 1016/j. neucom. 2004. 04. 005.
27. Yi{dotless}ldi{dotless}ri{dotless}m H, Alti{dotless}nsoy H, Bari{dotless}şçi{dotless} N, Ergün U, Oǧur E, Hardalaç F, Güler İ (2004) Classification of the frequency of carotid stenosis with MLP and RBF neural networks in patients with coroner artery disease. J Med Syst 28(6): 591-601.
28. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York.
29. Guler I, Ubeyli ED (2005) ECG beat classifier designed by combined neural network model. Pattern Recognit 38(2): 199-208. doi: 10. 1016/j. patcog. 2004. 06. 009.
30. Oztemel E (2003) Yapay sinir aǧlari{dotless} (Artificial Neural Networks). Papatya Publisher, Istanbul.
31. Lee JD (1997) Object recognition using a neural network with optimal feature extraction. Math Comput Modell 25(12): 105-117.
32. Moller MF (1993) A Scaled Conjugate-Gradient Algorithm for Fast Supervised Learning. Neural Netw 6(4): 525-533.
33. Francois D, Rossi F, Wertz V, Verleysen M (2007) Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70: 1276-1288.
34. Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intell 116: 1-16.