Abstract
Data gathered from real world processes include several undesired effects, like the noise in the process, the bias of the sensors and the presence of hysteresis, among other undesirable uncertainty sources. Learning models using the so called Low Quality Data (LQD) is a difficult task which has been barely studied. In a previous study, a method for learning white box models in the presence of LQD that makes use of Multi Objective Simulated Annealing hybridized with genetic operators method for learning models was proposed. This research studies the role of the tree generation methods when learning LQD. The results of this study show the relevance of the role of tree generation methods in the obtained results.
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Alcalá-Fdez, J., Sánchez, L., Garca, S., del Jesús, M.J., Ventura, S., Garrell i Guiu, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernndez, J.C., Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13(3), 307–318 (2009)
Banerjee, T.P., Das, S., Roychoudhury, J., Abraham, A.: Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds.) SOCO 2010. AISC, vol. 73, pp. 219–225. Springer, Heidelberg (2010)
Corchado, E., Arroyo, A., Tricio, V.: Soft computing models to Identify typical meteorological days. Logic J. of thel IGPL (2010), doi:10.1093/jigpal/jzq035
Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing (2010), doi:10.1016/j.asoc.2010.07.002
Couso, I., Sánchez, L.: Higher order models for fuzzy random variables. Fuzzy Sets Syst. 159, 237–258 (2008)
De Keyser, R., Ionescu, C.: Modelling and simulation of a lighting control system. Simul. Model. Pract. and Theory 18(2), 165–176 (2010)
Ferson, S., Kreinovich, V., Hajagos, J., Oberkampf, W., Ginzburg, L.: Experimental uncertainty estimation and statistics for data having interval uncertainty. RAMAS Technical Report SAND2007-0939 (2007), http://www.ramas.com/intstats.pdf
Folleco, A., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Identifying learners robust to low quality data. Informatica (Slovenia) 33(3), 245–259 (2009)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Li, D.H.W., Cheung, K.L., Wong, S.L., Lam, T.N.T.: An analysis of energy-efficient light fittings and lighting controls. Appl. Energy 87(2), 558–567 (2010)
Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Trans. on Evol. Comput. 4(3), 274–283 (2000)
Sánchez, L., Couso, I.: Advocating the use of imprecisely observed data in genetic fuzzy systems. IEEE Trans. on Fuzzy Systems 15(4), 551–562 (2007)
Sánchez, L., Otero, J.: Learning fuzzy linguistic models from low quality data by genetic algorithms. In: IEEE Int. Conf. on Fuzzy Systems FUZZ-IEEE, pp. 1–6 (2007)
Sánchez, L., Suárez, M.R., Villar, J.R., Couso, I.: Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data. Int. J. Approx. Reasoning 49, 607–622 (2008)
Sánchez, L., Couso, I., Casillas, J.: Genetic learning of fuzzy rules based on low quality data. Fuzzy Sets and Systems 160(17), 2524–2552 (2009)
Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integr. Computer-Aided Eng. 17(2), 103–115 (2010)
Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Proc. of the First Annual Conf. on Genetic Programming, GECCO 1996, pp. 215–223. MIT Press, Cambridge (1996)
Villar, J.R., Otero, A., Otero, J., Sánchez, L.: Taximeter verification with gps and soft computing techniques. Soft Comput. 14, 405–418 (2009)
Villar, J.R., de la Cal, E., Sedano, J.: A fuzzy logic based efficient energy saving approach for domestic heating systems. Integr. Computer-Aided Eng. 16, 151–163 (2009)
Villar, J.R., de la Cal, E., Sedano, J., García-Tamargo, M.: Analysing the low quality of the data in lighting control systems. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 421–428. Springer, Heidelberg (2010)
Villar, J.R., de la Cal, E., Sedano, J., García, M.: Evaluating the low quality measurements in lighting control systems. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds.) SOCO 2010. AISC, vol. 73, pp. 119–126. Springer, Heidelberg (2010)
Villar, J.R., Berzosa, A., de la Cal, E., Sedano, J., García-Tamargo, M.: Multi-objecve simulated annealing in genetic algorithm and programming learning with low quality data. Submitted to Neural Comput. (2010)
Yu, W.-D., Liu, Y.-C.: Hybridization of CBR and numeric soft computing techniques for mining of scarce construction databases. Autom. in Constr. 15(1), 33–46 (2006)
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Berzosa, A., Villar, J.R., Sedano, J., García-Tamargo, M. (2011). Tree Generation Methods Comparison in GAP Problems with Low Quality Data. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_10
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DOI: https://doi.org/10.1007/978-3-642-19644-7_10
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