GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Autor: Pernía Espinoza, Alpha VerónicaAntoñanzas Torres, Fernando Jesús; FERNANDEZ CENICEROS, JULIO; Martínez de Pisón Ascacíbar, Francisco Javier; SANZ GARCÍA, ANDRÉS; 

Tipo de documento: Artículo de revista

Revista: Applied Soft Computing Journal. ISSN: 1568-4946. Año: 2015. Volumen: 35. Páginas: 13-28.

Texto completo open access 

JCR (datos correspondientes al año 2014):
Edición:
Science  Área: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE  Quartil: Q1  Lugar área: 17/123  F. impacto: 2,810 
Edición:
Science  Área: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS  Quartil: Q1  Lugar área: 17/123  F. impacto: 2,810 

SCIMAGO (datos correspondientes al año 2014):
SJR:
2,22  SNIP: 2,729 

CIRC: GRUPO A - EXCELENCIA

Referencias:

  • Sanz-García, A., Antonanzas-Torres, F., Fernández-Ceniceros, J., Martínez-De Pisón, F.J., Overall models based on ensemble methods for predicting continuous annealing furnace temperature settings (2014) Ironmak. Steelmak., 41, pp. 51-60
  • Martínez-De-Pisón, F.J., Celorrio, L., Pérez-De-La-Parte, M., Castejón, M., Optimising annealing process on hot dip galvanising line based on robust predictive models adjusted with genetic algorithms (2011) Ironmak. Steelmak., 38 (3), pp. 218-228
  • Martínez-De Pisón, F.J., Pernía, A., González, A., López-Ochoa, L.M., Ordieres, J.B., Optimum model for predicting temperature settings on hot dip galvanising line (2010) Ironmak. Steelmak., 37 (3), pp. 187-194
  • Bloch, G., Sirou, F., Eustache, V., Fatrez, P., Neural intelligent control for a steel plant (1997) IEEE Trans. Neural Netw., 8 (4), pp. 910-918
  • Kim, Y., Moon, K., Kang, B., Han, C., Chang, K., Application of neural network to the supervisory control of a reheating furnace in the steel industry (1998) Control Eng. Pract., 6 (8), pp. 1009-1014
  • Pernía-Espinoza, A., Castejón-Limas, M., González-Marcos, A., Lobato-Rubio, V., Steel annealing furnace robust neural network model (2005) Ironmak. Steelmak., 32 (5), pp. 418-426
  • Yang, Y.-Y., Mahfouf, M., Pnoutsos, G., Development of a parsimonious ga-nn ensemble model with a case study for charpy impact energy prediction (2011) Adv. Eng. Softw., 42, pp. 435-443
  • Sanz-García, A., Fernández-Ceniceros, J., Fernández-Martínez, R., Martínez-De-Pisón, F.J., Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace (2014) Ironmak. Steelmak., 41 (2), pp. 87-98
  • Yang, Y., Linkens, D., Talamantes-Silva, J., Roll load prediction - data collection, analysis and neural network modelling (2004) J. Mater. Process. Technol., 152 (3), pp. 304-315
  • Helle, M., Saxen, H., Kerkkonen, O., Assessment of the state of the blast furnace high temperature region by tuyere core drilling (2009) ISIJ Int., 49 (2), pp. 203-209
  • Corchado, E., Grana, M., Wozniak, M., Editorial: New trends and applications on hybrid artificial intelligence systems (2012) Neurocomputing, 5 (1), pp. 61-63
  • Fernandez-Ceniceros, J., Sanz-Garcia, A., Antoanzas-Torres, F., De Pison, F.M., A numerical-informational approach for characterising the ductile behaviour of the t-stub component. part 2: Parsimonious soft-computing-based metamodel (2015) Eng. Struct., 82, pp. 249-260
  • Antonanzas-Torres, F., Sanz-Garcia, A., De Pisn, F.M., Antonanzas, J., Perpin-Lamigueiro, O., Polo, J., Towards downscaling of aerosol gridded dataset for improving solar resource assessment, an application to Spain (2014) Renew. Energy, 71, pp. 534-544
  • Antonanzas-Torres, F., Urraca, R., Antonanzas, J., Fernandez-Ceniceros, J., De Pison, F.M., Generation of daily global solar irradiation with support vector machines for regression (2015) Energy Convers. Manag., 96, pp. 277-286
  • Seni, G., Elder, J., (2010) Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions, , Morgan and Claypool Publishers
  • Reif, M., Shafait, F., Dengel, A., Meta-learning for evolutionary parameter optimization of classifiers (2012) Mach. Learn., 87 (3), pp. 357-380
  • Xue, B., Zhang, M., Browne, W.N., Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms (2014) Appl. Soft Comput., 18, pp. 261-276
  • Oduguwa, V., Tiwari, A., Roy, R., Evolutionary computing in manufacturing industry: an overview of recent applications (2005) Appl. Soft Comput., 5 (3), pp. 281-299
  • Caamano, P., Bellas, F., Becerra, J.A., Duro, R.J., Evolutionary algorithm characterization in real parameter optimization problems (2013) Appl. Soft Comput., 13 (4), pp. 1902-1921
  • Corchado, E., Wozniak, M., Abraham, A., De Carvalho, A.C.P.L.F., Snásel, V., Recent trends in intelligent data analysis (2014) Neurocomputing, 126, pp. 1-2
  • Valdez, F., Melin, P., Castillo, O., A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation (2014) Expert Syst. Appl., 41 (14), pp. 6459-6466
  • Huang, C.-J., Chen, Y.-J., Chen, H.-M., Jian, J.-J., Tseng, S.-C., Yang, Y.-J., Hsu, P.-A., Intelligent feature extraction and classification of anuran vocalizations (2014) Appl. Soft Comput., 19, pp. 1-7
  • Huang, H.-L., Chang, F.-L., ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data (2007) Biosystems, 90 (2), pp. 516-528
  • Ding, S., Spectral and wavelet-based feature selection with particle swarm optimization for hyperspectral classification (2011) J. Softw., 6 (7), pp. 1248-1256
  • Vieira, S.M., Mendonza, L.F., Farinha, G.J., Sousa, J.M., Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients (2013) Appl. Soft Comput., 13 (8), pp. 3494-3504
  • Huang, C.-L., Dun, J.-F., A distributed PSO-SVM hybrid system with feature selection and parameter optimization (2008) Appl. Soft Comput., 8 (4), pp. 1381-1391
  • Ahila, R., Sadasivam, V., Manimala, K., An integrated {PSO} for parameter determination and feature selection of {ELM} and its application in classification of power system disturbances (2015) Appl. Soft Comput., 32, pp. 23-37
  • Dhiman, R., Saini, J., Priyanka, Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures (2014) Appl. Soft Comput., 19, pp. 8-17
  • Castillo, O., Lizárraga, E., Soria, J., Melin, P., Valdez, F., New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system (2015) Inf. Sci., 294, pp. 203-215
  • Castillo, O., Neyoy, H., Soria, J., Melin, P., Valdez, F., A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot (2015) Appl. Soft Comput., 28, pp. 150-159
  • Winkler, S.M., Affenzeller, M., Kronberger, G., Kommenda, M., Wagner, S., Jacak, W., Stekel, H., Analysis of selected evolutionary algorithms in feature selection and parameter optimization for data based tumor marker modeling (2011) EUROCAST (1), Vol. 6927 of Lecture Notes in Computer Science, pp. 335-342. , R. Moreno-Diaz, R.Z.F. Pichler, A. Quesada-Arencibia, Springer
  • Chen, N., Ribeiro, B., Vieira, A., Duarte, J., Neves, J.C., A genetic algorithm-based approach to cost-sensitive bankruptcy prediction (2011) Expert Syst. Appl., 38 (10), pp. 12939-12945
  • Hastie, T., Tibshirani, R., Friedman, J., (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, , 2nd ed. Springer
  • Michalewicz, Z., Janikow, C.Z., Handling constraints in genetic algorithms (1991) ICGA, pp. 151-157
  • Vapnik, V.N., (1998) Statistical learning theory, , 1st ed. Wiley
  • Drucker, H., Chris, Kaufman, B.L., Smola, A., Vapnik, V., Support vector regression machines (1997) Adv. Neural Inf. Process. Syst., 9, pp. 155-161
  • Schölkopf, B., Smola, A.J., (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, , MIT Press Cambridge, MA, USA
  • Vapnik, V.N., (1995) The Nature of Statistical Learning Theory, , Springer-Verlag New York, Inc. New York, NY, USA
  • Smola, A.J., Schölkopf, B., A tutorial on support vector regression (2004) Stat. Comput., 14 (3), pp. 199-222
  • D.N.A. Asuncion, (2007) UCI machine learning repository, , http://www.ics.uci.edu/-mlearn/MLRepository.html
  • StatLib-Datasets Archive, , http://lib.stat.cmu.edu/datasets/
  • (2013) R: A Language and Environment for Statistical Computing, , R Foundation for Statistical Computing Vienna, Austria