Abstract
Experimental procedures associated with Machine Learning (ML) techniques are usually computationally demanding. An important step for a conscientious allocation of ML tasks into resources is predicting their execution times. Previously, empirical comparisons using a Meta-learning framework indicated that Support Vector Machines (SVM) are suited for this problem; however, their performance is affected by the choice of parameter values and input features. In this paper, we tackle the issue by applying Genetic Algorithm (GA) to perform joint Feature Subset Selection (FSS) and Parameters Optimization (PO). At first, a GA is used for FSS+PO in SVMs with two kernel functions, independently. Later, besides FSS+PO an additional term is evolved to weight predictions of both models to build a combined regressor. An empirical investigation conducted for predicting execution times of 6 ML algorithms over 78 publicly available datasets unveils a higher accuracy when compared with the previous results.
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References
Bensusan, H., Kalousis, A.: Estimating the Predictive Accuracy of a Classifier. In: Flach, P., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 25–36. Springer, Heidelberg (2001)
Braga, P.L., Oliveira, A.L.I., Meira, S.R.L.: A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation. In: Proceedings of ACM-SAC, pp. 1788–1792. ACM (2008)
Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61, 810–837 (2001)
Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer, Heidelberg (2009)
Chalimourda, A., Schölkopf, B., Smola, A.J.: Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Netw. 17, 127–141 (2004)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46(1-3), 131–159 (2002)
Congdon, C.B.: A comparison of genetic algorithm and other machine learning systems on a complex classification task from common disease research. Phd thesis, University of Michigan (1995)
Corchado, E., Abraham, A., de Carvalho, A.: Editorial: Hybrid intelligent algorithms and applications. Information Sciences 180, 2633–2634 (2010)
Corchado, E., Graña, M., Wozniak, M.: Editorial: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
Fei, S.-W., Sun, Y.: Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electric Power Systems Research 78(3), 507–514 (2008)
Fröhlich, H., Chapelle, O., Schölkopf, B.: Feature selection for support vector machines using genetic algorithms. International Journal on Artificial Intelligence Tools 13(4), 791–800 (2004)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Huang, C.-L., Dun, J.-F.: A distributed pso-svm hybrid system with feature selection and parameter optimization. Appl. Soft Comput. 8, 1381–1391 (2008)
Keerthi, S.S., Lin, C.-J.: Asymptotic behaviors of support vector machines with gaussian kernel. Neural Comput. 15, 1667–1689 (2003)
Lin, H.T., Lin, C.J.: A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Tech. rep., Department of Computer Science, National Taiwan University (2003)
Mejía-Guevara, I., Kuri-Morales, Á.: Evolutionary Feature and Parameter Selection in Support Vector Regression. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 399–408. Springer, Heidelberg (2007)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs (2nd, extended edn.). Springer-Verlag New York, Inc., New York (1994)
Priya, R., de Souza, B.F., Rossi, A.L.D., de Carvalho, A.C.P.L.F.: Predicting execution time of machine learning tasks using metalearning. In: World Congress on Information and Communication Technologies 2011, pp. 1197–1203. IEEE Computer Society (2011)
Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12, 1207–1245 (2000)
Uyar, S., Sariel, S., Eryigit, G.: A Gene Based Adaptive Mutation Strategy for Genetic Algorithms. In: Deb, K., et al. (eds.) GECCO 2004, Part II. LNCS, vol. 3103, pp. 271–281. Springer, Heidelberg (2004)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)
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Priya, R., de Souza, B.F., Rossi, A.L.D., de Carvalho, A.C.P.L.F. (2012). Using Genetic Algorithms to Improve Prediction of Execution Times of ML Tasks. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_18
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DOI: https://doi.org/10.1007/978-3-642-28942-2_18
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