Lytvynenko V.
In the paper, a classification method is proposed. It is based on Combined Swarm Negative Selection Algorithm, which was originally designed for binary classification problems. The accuracy of developed algorithm was tested in an experimental way with the use of microarray data sets. The experiments confirmed that direction of changes introduced in developed algorithm improves its accuracy in comparison to other classification algorithms.

1. Lockhart, D.J., Dong, H., Byrne, M.C., Follettie, M.T., Gallo, M.V., Chee, M.S., Mittmann, M., Wang, C., Kobayashi, M., Horton, H., Brown, E.L., 1996. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680.; 2. Schena, M., Shalon, D., Davis, R.W., Brown, P.O., 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470.; 3. Schena, M., Shalon, D., Heller, R., Chai, A., Brown, P.O., Davis, R.W., 1996. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. USA 93, 10614–10619. 4. Nguyen, D.V., Arpat, A.B., Wang, N., Carroll, R.J., 2002d. DNA microarray experiments: biological and technological aspects. Biometrics 58, 701–717.; 5. D.V. Nguyen, D.M. Rocke On partial least squares dimension reduction for microarray-based classi'cation: a simulation study / Computational Statistics & Data Analysis 46 (2004) 407 – 425.;6 Jayakishan M., Barik R. C., Ranjani P. M., S. K. Pradhan C.F. G. Dash Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data/ I.J. Information Technology and Computer Science, 2012, 9, 73-79.; 7. J. Khan, J. S. Wei, M. Ringner, L. H. Saal and M. Ladanyi et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nature Medicine 7 (2001) 673–679.;8. J. M. Deutsch, Evolutionary algorithms for finding optimal gene sets in microarray prediction, Bioinformatics19 (2003) 45–52.; 9. R. Tibshirani, T. Hastie, B. Narashiman and G. Chu, Diagnosis of multiple cancer types by shrunken centroids of gene expression, in Proc. Natl. Acad. Sci. USA, Vol. 99 (2002), pp. 6567–6572.;10. E. Bura and R. M. Pfeiffer, Graphical methods for class prediction using dimension reduction techniques on DNA microarray data, Bioinformatics 19 (2003) 1252–1258.; 11. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C.H., Angelo M. Ladd C., Reich M., Latulippe E., Mesirov J.P., Poggio T., Gerald W., Loda M., Lander E.S., Golub T.R., Multiclass cancer diagnosis using tumor gene expression signatures, Proc. Natl. Acad. Sci. USA. 98(26):15149-15154, (2001).; 12. Brown M.P.S., Grundy W., Lin D., Cristianini N., Sugnet C., Furey T., Ares M., Jr., Haussler D., Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA. 97(1):263-267, (2000). ; 13. Mukherjee S., Classifying microarray data using support vector machines, in Berrar D., Dubitzky W., Granzow M. (eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, Boston, pp. 166-185, (2002).; 14. Berrar, D.P., Downes, C.S., Dubitzky, W.: A Probabilistic Neural Network for Gene Selection and Classification of Microarray Data. ;In IC-AI(2003)342-352. ; 15. Meher J., Barik R. C., Panigrahi M. R., Pradhan S.K., Dash G. Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data / MECS I.J. Information Technology and Computer Science, 2012, 9, 73-79; 16. D. A. Salem, R.A. A. Seoud, H. A. Ali MGS-CM: A Multiple Scoring Gene Selection Technique for Cancer Classification using Microarrays /International Journal of Computer Applications (0975 – 8887) Volume 36– No.6, December 2011 p. 30 – 37.; 17. Dong Hoon Lim Principal Component Analysis using Singular Value Decomposition of Microarray Data/ World Academy of Science, Engineering and Technology 81, 2013, p.56-58.; 18. Wall M.E., Dyck P.A., Brettin T.S.(2001). SVDMAN – singular value decomposition analysis of microarray data. Bioinformatics 17:566-68.;19. Everitt, B. and T Hothorn. (2011). An Introduction to Applied Multivariate Analysis with R (Use R!). Springer, New York, NY.; 20. Jolliffe, I.T. (1986). Principal Component Analysis. Springer, New York.; 21. Massey, W.F. (1965) Principal components regression in exploratory statistical research. Journal of American Statistical Association, 60, 234-246. ; 22. Jian J. Dai, Linh Lieu, David Rocke Dimension Reduction for Classification with Gene Expression Microarray Data/ Stat Appl Genet Mol Biol. 2006;5:Article6. Epub 2006 Feb 24. 23. Babichev S.A., Babenko N.I., Dydik A.A. Lytvynenko V.I., Fefelov A.A., SV Shkurdoda Filtration chromatogram with pomoshchju veyvelet-analysis with Using Criteria entropyy (in Russion)/ Regional mizhvuzovskyy collection of scientific papers Issue 6 (71) Dnipropetrovsk 2010 C-17-3224. ; 24. Kennedy, J., Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks IV. pp. 1942–1948.doi:10.1109/ICNN.1995.488968.; 25. Shi, Y.; Eberhart, R.C. (1998). "A modified particle swarm optimizer". Proceedings of IEEE International Conference on Evolutionary Computation. pp. 69–73.; 26. Forrest, S., A.S. Perelson, L. Allen and R. Cherukuri, 1994. Self-nonself discrimination in a computer. Proceedings of the Symposium IEEE Computer Society on Research in Security and Privacy, May 16-18, IEEE Xplore Press, Oakland, CA., USA., pp: 202-212. DOI: 10.1109/RISP. 1994.296580; 27. Lytvynenko V. I.: Comparative experimental study of a modified negative selection algorithm and clonal selection algorithm negative for solving classification (in Russian), Vestnik Kherson National Technical University, nr 4(33), 2008, pp. 7-14. ;28. Lytvynenko V. I.: Immune classifier for solving binary classification - Theoretical Aspects (in Russian), System technologies, nr 1(42), 2006, pp. 32-47. ; 29. D. Dasgupta. Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine. November, 2006. - P.40-49.; 30. Kennedy, J. "The particle swarm: social adaptation of knowledge". Proceedings of IEEE International Conference on Evolutionary Computation. (1997). pp. 303–308.; 31. http://www.cs.waikato.ac.nz/ml/weka/; Lytvynenko V.I. Application of Clonal Negative Algorithm to Cancer Classification with DNA-Microarray Data// System technologies. – №6 (83). – Dnepropetrovsk, 2012. – P. 72 – 90. 32.Kennedy, J., Eberhart, R.C. (2001). Swarm Intelligence. Morgan Kaufmann. ISBN 1-55860-595-9. ; 33.Poli, R. (2007). "An analysis of publications on particle swarm optimization applications". Technical Report CSM-469 (Department of Computer Science, University of Essex, UK).; 34. Poli, R. (2008). "Analysis of the publications on the applications of particle swarm optimisation". Journal of Artificial Evolution and Applications 2008: 1–10.doi:10.1155/2008/685175.; 35. Mekhanet, Med.; Mokrani, L.; Lahdeb, Med. Comparison between Three Metaheuristics Applied to Robust Power System Stabilizer Design// Acta Electrotehnica . 2012, Vol. 53 Issue 1, p41-49. 9p.