A review of nearest neighbor-support vector machines hybrid classification models

Journal of Applied Sciences

Volumn 10, Issue 17, 2010, Pages 1841-1858

  Lee, Lam Hong ^a   Wan, Chin Heng ^a   Yong, Tien Fui ^a   Kok, Hui Meian ^a  

^a UNIVERSITI TUNKU ABDUL RAHMAN   (Malaysia)

Author keywords

Gene expression data classification; Machine learning; Pattern recognition; Spam e mail filtering; Visual category recognition

Indexed keywords

CONVOLUTION; GENE EXPRESSION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

CATEGORY RECOGNITION; CLASSIFICATION ALGORITHM; CLASSIFICATION MODELS; GENE EXPRESSION DATA; HYBRID CLASSIFICATION; K NEAREST NEIGHBOR (KNN); NEAREST NEIGHBOR ALGORITHM; SPAM E-MAILS;

CLASSIFICATION (OF INFORMATION);

EID: 77954832863     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2010.1841.1858     Document Type: Article

References (47)

1. Abd Alla, A.N., 2006. Three phase induction motor faults detection by using radial basis function neural network. J. Applied Sci., 6: 2817-2820.
2. Apte, C, F. Damerau and S.M. Weiss, 1994. Automated learning of decision rules for text categorization. ACM Trans. Inform. Syst, 12: 233-251.
3. Blanzieri, E. and A. Bryl, 2007a. Instance-based spam filtering using SVM nearest neighbor classifier. Proceedings of the 20th International Florida Artificial Intelligence Research Society Conference, May 7-9, Key West Florida, USA, pp: 441-442.
4. Blanzieri, E. and A. Bryl, 2007b. Evaluation of the highest probability SVM nearest neighbor classifier with variable relative error cost. Proceedings of the 4th Conference on Email and Anti- Spam, Aug. 2-3, Mountain View, California, USA., pp: 5-9.
5. Brown, M.P.S.D. Lin, W.N. Grundy, N. Cristianini and C. Sugnet et al, 1999. Support Vector Machine Classification of Microarray Gene Expression Data. University of California, Santa Cruz, California.
6. Brucher, H., G. Knolmayer and M.A. Mittermayer, 2002. Document classification methods for organizing explicit knowledge. Proceedings of the 3rd European Conference on Organizational Knowledge, Learning and Capabilities, (ECOKLC'02), Institute of Information Systems, University of Bern, Engehaldenstrasse Bern, Switzerland, Athens, Greece, pp: 124-126.
7. Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Min. Knowledge Discovery, 2: 121-167.
8. Chakrabarti, S., S. Roy and M.V. Soundalgekar, 2003. Fast and accurate text classification via multiple linear discriminant projection. VLDB J., 12: 170-185.
9. Domingos, P. and M. Pazzani, 1997. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29: 103-130.
10. Dror, G., R. Sorek and R. Shamir, 2005. Accurate identification of alternatively spliced exons using support vector machine. Bioinfoimatics, 21: 897-901.
11. Drucker, PL, D. Wuand V.N. Vapnik, 1999. Support vector machines for spam categorization. IEEE Trans. Neural Network, 10: 1048-1054.
12. Eyheramendy, S., A. Genkin, W.H. Ju, D. Lewis and D. Madigan, 2003. Sparce bayesian classifiers for text categorization. Department of Statistics, Rutgers University. http://www.stat.rutgers.edu/~madigan/PAPERS/jicrd-vl3.pdf.
13. Godbole, S., 2006. Inter-class relationship in text classification. PhD. Thesis, Indian Institute of Technology, Bombay, India, http://www.it.iitb.ac.in/-shantanu/work/thesis.pdf
14. Greiner, R. and J. Schaffer, 2001. AIxploratorium-Decision Trees. Department of Computing Science, Alberta, Canada.
15. Gutschoven, B. and P. Verlinde, 2000. Multi-modal identity verification using support vector machines (SVM). Proc. 3rd Int. Conf. Inform. Fusion, 2: 3-8.
16. Han, E.H., G. Karypis and V. Kumar, 1999. Text categorization using weight adjusted k-nearest neighbour classification. Department of Computer Science and Engineering, Army HPC Research Center, University of Minnesota, http://glaros.dtc.umn.edu/gkhome/fetch/papers/wknnPAKDD01.pdf.
17. Haykin, S., 1999. Neural Networks: A Comprehensive Foundation. 2nd Edn., Prentice Hall, New Jersey, USA., ISBN: 8120323734, pp: 443-484.
18. Isa, D., L.H. Lee, V.P. Kallimani and R. Prasad, 2008a. Polychotomiser for case-based reasoning beyond the traditional bayesian classification approach. Comput. Inform. Sci., 1: 57-68.
19. Isa, D., L.H. Lee, V.P. Kallimani and R. Rajkumar, 2008b. Text document pre-processing with the bayes formula for classification using the support vector machine. IEEE Trans. Knowledge Data Eng., 20: 1264-1272.
20. Isa, D., L.H. Lee, V.P. Kallimani and R. Rajkumar, 2008c. Text document pre-processing with the bayes formula for classification based on the vector space model. Comput. Inform. Sci., 1: 79-90.
21. Isa, D., V.P. Kallimani and L.H. Lee, 2009. Using the self-organizing map for clustering of text documents. Expert Syst. Appl., 36: 9584-9591.
22. Joachims, T., 1999. Making Large-Scale SVM Learning Practical. In: Advances in Kernel Methods-Support Vector Learning, Scholkopf, B., C.J.C. Burges and A.J. Smola,(Eds.). MIT Press, Cambridge, MA., ISBN-10: 0-262-19416-3, pp: 169-184.
23. Joachims, T., 1998. Text categorization with support vector machines: Learning with many relevant features. Proceedings of 10th European Conference on Machine Learning, Chemnitz, Germany, Apr. 21-23, Springer Berlin/Heidelberg, pp: 137-142.
24. Kim, S.B., H.C. Rim, D.S. Yook and H.S. Lim, 2002a. Effective methods for improving naive bayes text classifiers. Lecture Notes Comput. Sci., 2417: 479-484.
25. Kim, K.I., K.C. Jung, S.H. Park and H.J. Kim, 2002b. Support vector machines for texture classification. IEEE Trans. Pattern Anal. Machine Intell.,24: 1542-1550.
26. Lee, L.H., 2008. Domain specific classification and hybrid classification techniques for text document categorization. Ph.D. Thesis, The University of Nottingham. http://www.nottingham.edu.my/Faculties/Science/CSIT/Documents/ThesisLam.pdf.
27. Lee, L.H., D. Isa, W.O. Choo and W.Y. Chue, 2010. Tournament structure ranking techniques for Bayesian text classification with highly similar categories. J. Applied Sci., 10: 1243-1254.
28. Li, S., J.T. Kwok, H. Zhu and Y. Wang, 2003. Texture classification using the support vector machines. Pattern Recognition, 36: 2883-2893.
29. Lin, Y., 1999. Support vector machines and the bayes rule in classification. Technical Report No. 1014, Department of Statistics, University of Wiscousin, Madison, http://www.stat.wisc.edu/public/ftp/yilin/trl014.ps.
30. McCallum, A. and K. Nigam, 2003. A comparison of event models for naive bayes bayes text classification. J. Machine Learning Res., 3: 1265-1287.
31. Mei, Z., Q. Shen and B. Ye, 2009. Hybridized KNN and SVM for gene expression data classification. Life Sci. J.,6: 61-66.
32. Osuna, R.G., 2002. Lecture Notes CS 790: Introduction to Pattern Recognition. Wright State University, Dayton, Ohio, USA.
33. Peng, J., D.R. Heisterkamp and H.K. Dai, 2003. LDA/SVM driven nearest neighbor classification. IEEE Trans. Neural Networks, 14: 940-942.
34. Quinlan, J.R., 1993. C4.5: Program for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA., ISBN: 1-55860-238-0, pp: 302.
35. Rangwala, H. and G. Karypis, 2005. Profile-based direct kernels for remote homology detection and fold recognition. Bioinformatics, 21: 4239-4247.
36. Rish, I., 2001. An empirical study of the naive bayes classifier. Proceedings of the IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence, (WEMAI'Ol), Watson Research Center, Hawthorne, pp: 41-46.
37. Sani, M.M., K.A. Ishak and S.A. Samad, 2009. Classification using adaptive multiscale retinex and support vector machine for face recognition system. J. Applied Sci., 10: 506-511.
38. Shen, X. and Y. Lin, 2004. Gene expression data classification using SVM-KNN classifier. Proceedings of the International Symposium on Intelligent Multimedia, Video and Speech Processing, Oct. 20-22, Hong Kong, China, pp: 149-152.
39. Shin, K., A. Abraham and S. Y. Han, 2006. Improving kNN text categorization by removing outliers from training set. Proceedings of the 7th International Conference of Computational Linguistics and Intelligent Text Processing, Feb. 19-25, Mexico City, Mexico, Germany, pp: 563-566.
40. Soltanizadeh, H. and B.S. Shahriar, 2008. Feature extraction and classification of objects in the rosette pattern using component analysis and neural network. J. Applied Sci., 8: 4088-4096.
41. Tan, S., 2006. An effective refinement strategy for KNN Text classifier. Expert Syst. Appl., 30: 290-298.
42. Tao, D., X. Tang, X. Li and X. Wu, 2006. Asymmetric bagging and random subspace for support vector machines based relevance feedback in image retrieval. IEEE Trans. Patt. Anal. Mach. Int., 28: 1088-1099.
43. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Ist Edn., Springer-Verlag, New York, ISBN: 0-387-94559-8.
44. Yang, Y. and X. Liu, 1999. A re-examination of text categorization method. Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 15-19, New York, USA., pp: 42-49.
45. Yang, Y.M. and J. Pedersen, 1997. A comparative study on feature selection in text categorization. Proceedings of the 14th International Conference on Machine Learning, (ICML'97), Morgan Kaufmann, pp: 412-420.
46. Zhang, H., A.C. Berg, M. Maire and J. Malik, 2006. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Volume 2, June 17-22, Washington, DC, USA., pp: 2126-2136.
47. Zhang, J.P., 2004. Chapter 6: The Structural Risk Minimization Principle. Intelligent Information Processing Laboratory, Fudan.