A novel hybrid system for diagnosing breast cancer using fuzzy rough set and LS-SVM

Research Journal of Applied Sciences, Engineering and Technology

Volumn 10, Issue 1, 2015, Pages 49-55

  Jaya Suji, R ^a   Rajagopalan, S P ^b  

^a SATHYABAMA UNIVERSITY   (India)

^b GKM COLLEGE OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

Cancer diagnosis; Data mining; K means clustering; Least square support vector machine

Indexed keywords

EID: 84931299801     PISSN: 20407459     EISSN: 20407467     Source Type: Journal    
DOI: 10.19026/rjaset.10.2553     Document Type: Article

References (22)

1. Chen, C.H., 2014. A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection. Appl. Soft Comput., 20: 4-14.
2. Chen, M.S., J. Han and P.S. Yu, 1996. Data mining: An overview from a database perspective. IEEE T. Knowl. Data En., 8: 866-883.
3. Cortes, C. and V. Vapnik, 1995. Support-vector networks. Mach. Learn., 20: 273-297.
4. Dubois, D. and H. Prade, 1990. Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst., 17: 191-208.
5. Fayyad, U., G. Piatetsky-Shapiro and P. Smyth, 1996. From data mining to knowledge discovery in databases. Artif. Intell. Mag., 17: 37-54.
6. Ferreira, A.J. and M.A.T. Figueiredo, 2012. An unsupervised approach to feature discretization and selection. Pattern Recogn., 45: 3048-3060.
7. Ganapathy, S., P. Yogesh and A. Kannan, 2012b. Intelligent agent based intrusion detection system using enhanced multiclass SVM. Int. J. Comput. Intell. Neurosci., 20(12): 195-202.
8. Ganapathy, S., K. Kulothungan, P. Yogesh and A. Kannan 2012a. A novel weighted fuzzy c-means clustering based on immune genetic algorithm for intrusion detection. Proc. Eng., 38: 1750-1757.
9. Ganapathy, S., K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh and A. Kannan, 2013. Intelligent feature selection and classification techniques for intrusion detection in networks: A survey. EURASIP J. Wirel. Comm., 271: 1-16.
10. Hall, M., 1998. Correlation-based feature selection for machine learning. Ph. D. Thesis, Department of Computer Science, Waikato University, Hamilton, NZ.
11. Jain, A.K., M.N. Murty and P.J. Flynn, 1999. Data clustering: A review. ACM Comput. Surv., 31: 264-323.
12. Jaisankar, N., S. Ganapathy and A. Kannan, 2012. Intelligent intrusion detection system using fuzzy rough set based C4.5 algorithm. Proceeding of International ACM Conference on Advances in Computing, Communications and Informatics (ICACCI-2012), pp: 596-601.
13. Jensen, R. and Q. Shen, 2004. Fuzzy-rough attributes reduction with application to web categorization. Fuzzy Set. Syst., 141(3): 469-485.
14. Li, M., S. Deng, L. Wang, S. Feng and J. Fan, 2014. Hierarchical clustering algorithm for categorical data using a probabilistic rough set model. Knowl-Based Syst., 65: 60-71.
15. Polat, K. and S. Günes, 2007. Breast cancer diagnosis using least square support vector machine. Digit. Signal Process., 17: 694-701.
16. Prasad, Y., K. Biswas and C. Jain, 2010. SVM classifier based feature selection using GA, ACO and PSO for siRNA design. Proceeding of the 1st International Conference on Advances in Swarm Intelligence, pp: 307-314.
17. Siegel, R., D. Naishadham and A. Jemal, 2012. Cancer statistics. Cancer J. Clin., 62: 10-29.
18. Venkatadri, M. and C.R. Lokanatha, 2011. A review on data mining from past to the future. Int. J. Comput. Appl., 15: 19-22.
19. Wolberg, W.H., W.N. Street and O.L. Mangasarian, 1995. Image analysis and machine learning applied to breast cancer diagnosis and proganosis. Anal. Quant. Cytol., 17(2): 77-87.
20. Xu, R. and D. Wunsch, 2005. Survey of clustering algorithms. IEEE T. Neural Networ., 16: 645-678.
21. Zadeh, L.A., 1965. Fuzzy sets. Inform. Control, 8: 338-353.
22. Zheng, B., S.W. Yoon and S.S. Lam, 2014. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl., 41: 1476-1482.