A hybrid classifier based on SVM method for cancer classification

Weronika Piątkowska, Jerzy Martyna


In this paper, we proposed a new method of applying Support Vector Machines (SVMs) for cancer classification. We proposed a hybrid classifier that considers the degree of a membership function of each class with the help of Fuzzy Naive Bayes (FNB) and then organizes one-versus-rest (OVR) SVMs as the archi¬tecture classifying into the corresponding class. In this method, we used a novel system of ordering the recognized expression profiles by means of using FNB and genering SVMs with the OVR scheme. The results show that our hybrid classifier is comparable to the conventional methods.


SVM method; Fuzzy Naive Bayes; cancer classification

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Brown P. O., Brotstein D.: Exploring the New World of the Genome with DNA Microarrays. Nat. Genet. Suppl., 21, 1999, p. 33-37.

Cho S. -B., Ryu J.: Classifying Gene Expression Data of Cancer Using Classifier Ensemble with Mutually Exclusive Features. Proc. IEE 90 (11), 2002, p. 1744-1753.

Cortes C, Vapnik V. N.: Support Vector Networks. Machine Learning, 20, 1995, p. 273-297.

Duggan D. J., Bittner M, Chen Y., Melter P., Trent J.: Expression Profiling Using cDNA Microarrays. Nature Genetics, 21, 1999, p. 10-14.

Jeffrey R. C: The Logic of Decision. Gordon and Brench Inc., New York 1965.

Lipschutz R. J., Fodor S. P. A., Gingeras T. R., Lockhart D. J.: High Density Synthetic Eigenuclectide Arrays. Nature Genetics, 21, 1999, p. 20-24.

Liu J., et al.: An Improved Naive Bayesian Classifier Technique Coupled with a Novel Input Solution Method. IEEE Trans, on Systems, Man, and Cybernetics - Part C: Appl. Rev. 31, No. 2, 2001, p. 249-256.

McLachlan G. J., Do K. -A., Ambroise Ch.: Analyzing Microarray Gene Expression Data. John Wiley and Sons, 2004.

Muller K. R., Mike S., Ratsch G, Tsuda K., Scholkopf B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Trans. On Neural Networks, Vol. 12, No. 2, 2001, p. 181-201.

Ramaswamy S., et al: Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures. Proc. Nat. Acad. Sci., Vol. 98, No. 26, 2001, p. 15149-15154.

Randon J., Ławry J.: Classification and Query Evaluation Using Modeling with Words. Information Sciences. Special Issue - Computing with Words: Models and Applications, Vol. 176, 2006, p. 438-464.

Vapnik V. N.: The Nature of Statistical Learning Theory. Springer-Verlag, Berlin, Heidelberg, New York 1995.

Vapnik V. N.: Statistical Learning Theory. John Wiley and Sons, 1998.

Vapnik V. N.: The Support Vector Method of Function Estimation, in: J. A. K. Suykens, J. Vandewolle (eds.). Nonlinear Modeling: Advanced Black-box Techniques, Kluwer Academic Publishers, Boston 1998, p. 55-85.

DOI: http://dx.doi.org/10.21936/si2009_v30.n2A.492