International Journal of Information Engineering and Electronic Business(IJIEEB)
ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)
Published By: MECS Press
IJIEEB Vol.5, No.5, Nov. 2013
Performance Evaluation of Bagged RBF Classifier for Data Mining Applications
Full Text (PDF, 292KB), PP.49-56
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with radial basis function classifier as the base learner. The proposed bagged radial basis function is superior to individual approach for data mining applications in terms of classification accuracy.
Cite This Paper
M.Govindarajan,"Performance Evaluation of Bagged RBF Classifier for Data Mining Applications", IJIEEB, vol.5, no.5, pp.49-56, 2013. DOI: 10.5815/ijieeb.2013.05.07
A. Amin, H. B. Al-Sadoun, and S. Fischer, Hand-printed Arabic Character Recognition System Using An Artificial Network, Pattern Recognition, 1996, 29(4):663-675.
J.P. Anderson, Computer security threat monitoring and surveillance, Technical Report, James P. Anderson Co., Fort Washington, PA, 1990.
Bentz, Y., Merunkay, D. Neural Networks and the Multinomial for Brand Choice Modeling, A Hybrid Approach." Journal of Forcasting, 2000, 19(3): 177-200.
E. Biermann, E. Cloete and L.M. Venter, (2001), "A comparison of intrusion detection Systems", Computer and Security, 2001, 20:676-683.
Breiman, L. Stacked Regressions, Machine Learning, 1996c, 24(1):49-64.
Bounds, D., Ross, D. Forcasting Customer Response with Neural Network, Handbook of Neural Computation, 1997, G6.2: 1-7.
J. Cai, M. Ahmadi, and M. Shridhar, (1995), “Recognition of Handwritten Numerals with Multiple Feature and Multi-stage Classifier”, Pattern Recognition, 1995, 28(2):153-160.
Ghosh AK, Schwartzbard A. A study in using neural networks for anomaly and misuse detection. In: The proceeding on the 8th USENIX security symposium, http://citeseer.ist.psu.edu/context/1170861/0,1999.
Ha, k., S. Cho, et al. Response Models Based on Bagging Neural Networks, Journal of Interactive Marketing, 2005,19: 17-30
Heady R, Luger G, Maccabe A, Servilla M. The architecture of a network level intrusion detection system. Technical Report, Department of Computer Science, University of New Mexico, 1990.
T.K.Ho, J.J.Hull, and S.N.Srihari, Combination of Structural Classifiers, in Proc. IAPR Workshop Syntatic and Structural Pattern Recog., 1990, 123-137.
Hu, X. A data mining approach for retailing bank customer attrition analysis, Applied Intelligence, 2005, 22(1):47-60.
K. Ilgun, R.A. Kemmerer and P.A. Porras. State transition analysis:A rule-based intrusion detection approach, IEEE Trans. Software Eng, 1995, 21:181-199.
Ira Cohen, Qi Tian, Xiang Sean Zhou and Thoms S.Huang, Feature Selection Using Principal Feature Analysis, In Proceedings of the 15th international conference on Multimedia, Augsburg, Germany, September 2007, 25-29.
Jiawei Han, Micheline Kamber. Data Mining – Concepts and Techniques, Elsevier Publications, 2003.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of International Joint Conference on Artificial Intelligence, 1995, 1137–1143.
Li, W., Wu, X., Sun, Y. and Zhang, Q., Credit Card Customer Segmentation and Target Marketing Based on Data Mining, In Proceedings of International Conference on Computational Intelligence and Security, 2010, 73-76.
Ling, X. and Li, C., Data Mining for Direct Marketing: Problems and Solutions. In Proceedings of the 4th KDD conference, AAAI Press, 1998, 73–79.
E. Lundin and E. Jonsson. Anomaly-based intrusion detection: privacy concerns and other problems, Computer Networks, 2002, 34: 623-640.
D. Marchette. A statistical method for profiling network traffic. In proceedings of the First USENIX Workshop on Intrusion Detection and Network Monitoring (Santa Clara), CA. 1999: 119-128.
Mukkamala S, Sung AH, Abraham A. Intrusion detection using ensemble of soft computing paradigms, third international conference on intelligent systems design and applications, intelligent systems design and applications, advances in soft computing. Germany: Springer; 2003:239–48.
Mukkamala S, Sung AH, Abraham A. Modeling intrusion detection systems using linear genetic programming approach, The 17th international conference on industrial & engineering applications of artificial intelligence and expert systems, innovations in applied artificial intelligence. In: Robert O., Chunsheng Y., Moonis A., editors. Lecture Notes in Computer Science, vol. 3029. Germany: Springer, 2004a: 633–42.
Mukkamala S, Sung AH, Abraham A, Ramos V. Intrusion detection systems using adaptive regression splines. In: Seruca I, Filipe J, Hammoudi S, Cordeiro J, editors. Proceedings of the 6th international conference on enterprise information systems, ICEIS’04, vol. 3, Portugal, 2004b: 26–33.
S. Mukkamala, G. Janoski and A.Sung. Intrusion detection: support vector machines and neural networks, In proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO, 2002, 1702-1707.
Oliver Buchtala, Manuel Klimek, and Bernhard Sick, Member, IEEE, Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications, IEEE Transactions on systems, man, and cybernetics—part b: cybernetics, 2005, 35(5).
Schapire, R., Freund, Y., Bartlett, P., and Lee, W. Boosting the margin: A new explanation for the effectives of voting methods. In proceedings of the fourteenth International Conference on Machine Learning, Nashville, TN, 1997, 322-330.
Shah K, Dave N, Chavan S, Mukherjee S, Abraham A, Sanyal S. Adaptive neuro-fuzzy intrusion detection system. IEEE International Conference on Information Technology: Coding and Computing (ITCC’04), USA: IEEE Computer Society, 2004, (1):70–74.
T. Shon and J. Moon. A hybrid machine learning approach to network anomaly detection, Information Sciences, 2007, (177):3799-3821.
C.Y.Suen, C.Nadal, T.A.Mai, R.Legault, and L.Lam. Recognition of totally unconstrained handwritten numerals based on the concept of multiple experts, Frontiers in Handwriting Recognition , C.Y.Suen, Ed., IN Proc.Int.Workshop on Frontiers in Handwriting Recognition, Montreal, Canada, Apr. 2-3, 1990, 131-143.
C. Y. Suen, C. Nadal, R. Legault, T. A. Mai, and L. Lam. Computer recognition of unconstrained handwritten numerals,” Proc. IEEE, 1992, (80):1162–1180.
Summers RC. Secure computing: threats and safeguards. New York, McGraw-Hill, 1997.
Sundaram A. An introduction to intrusion detection. ACM Cross Roads, 1996, 2(4).
W. Stallings. Cryptography and network security principles and practices, USA: Prentice Hall, 2006.
Tang, Z. Improving Direct Marketing Profitability with Neural Networks. International Journal of Computer Applications, 2011, 29(5): 13-18.
C. Tsai, Y. Hsu, C. Lin and W. Lin. Intrusion detection by machine learning: A review, Expert Systems with Applications, 2009, (36):11994-12000.
T. Verwoerd and R. Hunt. Intrusion detection techniques and approaches, Computer Communications, 2002, (25):1356-1365.
Viaene, S., B. Baesens, et al. Wrapped Input Selection Using Multilayer Perceptrons for Repeat-Purchase Modeling in Direct Marketing, International Journal of Intelligent Systems in Accounting, Finance and Management, 2001, 10(2): 115-126.
S. Wu and W. Banzhaf. The use of computational intelligence in intrusion detection systems: A review, Applied Soft Computing, 2010, (10):1-35.