Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms
Tóm tắt
The classification and analysis of data is an important issue in today’s research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.
Tài liệu tham khảo
Raghupathi, W., Data mining in health care. Health. Informat. Improv. Efficienc. Productiv. 211–223, 2010.
Piateski, G., and Frawley, W., Knowledge discovery in databases. MIT press: Cambridge, MA, USA, 1991.
Mannila, H., Data mining: machine learning, statistics, and databases. In: Eighth International Conference on Scientific and Statistical Database Systems (pp. 2–9). IEEE Computer Society: Stockholm, 1996.
Dash, M., and Liu, H., Feature selection for classification. Intell. Data Anal. 1(3):131–156, 1997.
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906–914, 2000.
Livadas, C., Walsh, R., Lapsley, D., and Strayer, W. T., Using machine learning techniques to identify botnet traffic. In: The 31st IEEE Conference on Local Computer Networks (pp. 967–974). IEEE: Tampa, FL, 2006.
Shin, C., Doermann, D., and Rosenfeld, A., Classification of document pages using structure-based features. Int. J. Doc. Anal. Recog. 3(4):232–247, 2001.
Liu, H., and Motoda, H., Feature selection for knowledge discovery and data mining. Kluwer Academic Publishers: Norwell, MA, USA, 1998.
Holland, J. H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press: Cambridge, MA, USA, 1992.
Kirkpatrick, S., and Vecchi, M. P., Optimization by simmulated annealing. Science 220(4598):671–680, 1983.
Cortes, C., and Vapnik, V., Support-vector networks. Mach. Learn. 20(3):273–297, 1995.
Kennedy, J., Particle swarm optimization. Encyclopedia of Machine Learning (pp. 760–766), Springer, US, 1995.
Chen, D. B., and Zhao, C. X., Particle swarm optimization based on endocrine regulation mechanism. Contr. Theor. Appl. 24(6):126–134, 2007.
Karaboga, D., An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
Youssef, H., Sait, S. M., and Adiche, H., Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Eng. Appl. Artif. Intel. 14(2):167–181, 2001.
Wang, X., Hybrid nature-inspired computation methods for optimization. TKK Dissertations, Doctoral Dissertation, Helsinki University of Technology, 2009.
Zhi-gang, W., Hybrid optimization algorithm based on particle swarm optimization and artificial bee colony algorithm. Sci. Technol. Eng. 12(20):4921–4925, 2012.
Guo, Z., A hybrid optimization algorithm based on artificial bee colony and gravitational search algorithm. Int. J. Dig. Cont. Tech. Appl. 6(17):620–626, 2012.
Karaboga, D., and Akay, B., A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1):108–132, 2009.
Liu, J., Zhang, X., and Ning, A., Hybrid optimization algorithm of PSO and ABC. Comput. Eng. Appl. 47(35):32–34, 2011.
Altun, O., and Korkmaz, T., Particle Swarm Optimization–Artificial Bee Colony Chain (PSOABCC): A hybrid meteahuristic algorithm. Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields (pp. 22–23). Istanbul, Turkey: Koc University, 2014.
Kong, X., Liu, S., and Wang, Z., A new hybrid artificial bee colony algorithm for global optimization. Int. J. Comp. Sci. 10(1), 2013.
Hsu, C. W., Chang, C. C., and Lin, C. J., A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003.
Lichman, M., UCI repository of machine learning databases. Irvine, CA: University of California, School of Information and Computer Science. [http://www.ics.uci.edu/∼mlearn/MLRepository.html], 2013.
Salzberg, S. L., On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Min. Knowledg. Discov. 1(3):317–328, 1997.
Shi, Y., and Eberhart, R. C., Empirical study of particle swarm optimization. In Proceedings of the Congress on Evolutionary Computation (CEC '99) (pp. 1945–1950). IEEE Service Center, Piscataway, NJ, USA, 1999.