[1] | Joakim Storck and Bengt Lindberg. A cost model for the effect of setup time reduction in stainless steel stripproduction. In Swedish Production Symposium : 2007. |
[2] | R. D. King, C. Feng, and A. Sutherland. Statlog: Comparison of classification algorithms on large real-worldproblems. Applied Artificial Intelligence, 9(3):289–333, 1995. |
[3] | Rich Caruana and Alexandru Niculescu-Mizil. An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning, ICML ’06, pages 161–168, New York, NY, USA, 2006. ACM. |
[4] | Tjen-Sien Lim, Wei-Yin Loh, and Yu-Shan Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3):203–228, 2000. |
[5] | David Meyer, Friedrich Leisch, and Kurt Hornik. The support vector machine under test. Neurocomputing, 55(1â€“2):169 – 186, 2003. Support Vector Machines. |
[6] | Thomas M. Mitchell. Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 1997. |
[7] | Leo Breiman. Statistical modeling: The two cultures (with commentsand a rejoinder by the author). Statist. Sci., 16(3):199–231, 08 2001. |
[8] | M.R.G. Meireles, P.E.M. Almeida, and M.G. Simoes. A comprehensive review for industrial applicability of artificial neural networks. Industrial Electronics, IEEE Transactions on, 50(3):585–601, June 2003. |
[9] | G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4):303–314, 1989. |
[10] | D. Randall Wilson and Tony R. Martinez. The general inefficiency of batch training for gradient descent learning. Neural Netw., 16(10):1429–1451, December 2003. |
[11] | Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, May 2015. Insight. |
[12] | N.J. Bershad, J.J. Shynk, and P.L. Feintuch. Statistical analysis of the single-layer backpropagation algorithm. ii. mse and classification performance. Signal Processing, IEEE Transactions on, 41(2):581–591, Feb 1993. |
[13] | James Bergstra and Yoshua Bengio. Random search forhyper-parameter optimization. J. Mach. Learn. Res., 13(1):281–305, February 2012. |
[14] | P.K. Sharpe and R.J. Solly. Dealing with missing values in neural network-based diagnostic systems.Neural Computing and Applications, 3(2):73–77, 1995. |
[15] | V Vapnik and A Lerner. Pattern recognition using generalized portrait method. Automation and Remote Control, 24, 1963. |
[16] | Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. A trainingalgorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshopon Computational Learning Theory, COLT ’92, pages 144–152, New York, NY, USA, 1992. ACM. |
[17] | Corinna Cortes and Vladimir Vapnik. Support-vector networks. Mach. Learn., 20(3):273–297, September 1995. |
[18] | Alain Rakotomamonjy. Support vector machines and area under roc curves. Technical report, 2004. |
[19] | Eyke HÃ¼llermeier and Stijn Vanderlooy. An empirical and formal analysis of decision trees for ranking. |
[20] | Leo Breiman. Random forests. Mach. Learn., 45(1):5–32, October 2001. |
[21] | He Dawei Harley Ronald Habetler Thomas Qu Guannan Mei, Jie. Arandom forest method for real-time price forecasting in new york electricity market. |
[22] | Leo Breiman. Bagging predictors. Machine Learning, 24(2):123–140, 1996. |
[23] | Pengyi Yang, Yee Hwa Yang, Bing B. Zhou, and Albert Y. Zomaya. A review of ensemble methods in bioinformatics. Current Bioinformatics, 5(4), 2010-12-01T00:00:00. |
[24] | Robert E. Schapire. The strength of weak learnability. Mach. Learn., 5(2):197–227, July 1990. |
[25] | Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119–139, August 1997. |
[26] | Philip M. Long and Rocco A. Servedio. Random classification noise defeats all convex potential boosters. In Machine Learning, Proceedings of theTwenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008, pages 608–615, 2008. |
[27] | Tom Fawcett. An introduction to roc analysis. Pattern Recogn. Lett., 27(8):861–874, June 2006. |
[28] | Rich Caruana and Alexandru Niculescu-Mizil. Data mining in metric space: An empirical analysis of supervised learning performance criteria. pages 69–78. ACM Press, 2004. |
[29] | John C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihoodmethods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61–74. MIT Press, 1999. |
[30] | Bianca Zadrozny and Charles Elkan. Transforming classifier scores intoaccurate multiclass probability estimates, 2002. |
[31] | Jan de Leeuw, Kurt Hornik, and Patrick Mair. Isotone optimization in r: Pool-adjacent-violators algorithm (pava) methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61–74. MIT Press, 1999. |
[32] | Alexandru Niculescu-mizil and Rich Caruana. Predicting goodprobabilities with supervised learning. In In Proc. Int. Conf. on Machine Learning(ICML, pages 625–632, 2005. |
[33] | Isabelle Guyon and André Elisseeff. An introduction to variable andfeature selection. J. Mach. Learn. Res., 3:1157–1182, March 2003. |
[34] | Mark A. Hall. Correlation-based feature selection for discrete and numeric class machine learning. pages 359–366. Morgan Kaufmann, 2000. |
[35] | Lei Yu and Huan Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. pages 856–863, 2003. |
[36] | Houtao Deng and George C. Runger. Feature selection via regularizedtrees. CoRR, abs/1201.1587, 2012. |
[37] | Houtao Deng and George Runger. Gene selection with guided regularized random forest. Pattern Recognition, 46(12):3483 – 3489, 2013. |
[38] | Robert Tibshirani. Regression shrinkage and selection via thelasso. Journal of the Royal Statistical Society, Series B, 58:267–288, 1994. |
[39] | M.R. Kandroodi and B. Moshiri. Identification and model predictive control of continuous stirred tank reactor based on artificial neural networks. In Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on, pages 338–343, Dec 2011. |
[40] | A. Singh and A. Narain. Neural network based predictive control for nonlinear chemical process. In Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on, pages 321–326, Oct 2010. |
[41] | Feifei Wang, S. Sanguansintukul, and C. Lursinsap. Curl forecasting for paper quality in papermaking industry. In System Simulation andScientific Computing, 2008. ICSC 2008. Asia Simulation Conference - 7th International Conference on, pages 1079–1084, Oct 2008. |
[42] | Mahmoud Reza Saybani, Teh Ying Wah, Amineh Amini, SRAS Yazdi, and Adel Lahsasna. Applications of support vector machines in oil refineries: A survey. International Journal of Physical Sciences, 6(27):6295–6302, 2011. |
[43] | Zhe Xu and Zhizhong Mao. Comparisons of element yield rate prediction using feed-forward neural networks and support vector machine. In Control and Decision Conference (CCDC), 2010 Chinese, pages 4163–4166, May 2010. |
[44] | Haisheng Li and Xuefeng Zhu. Application of support vector machine method in prediction of kappa number of kraft pulping process. In IntelligentControl and Automation, 2004. WCICA 2004. Fifth World Congress on, volume 4, pages 3325–3330 Vol.4, June 2004. |
[45] | Coopersmith Ellen, Dean Graham, McVean Jason, and StorauneErling. Making decisions in the oil and gas industry. Oilfield Review, 12(4), 2000. |
[46] | S.R. Aghdam, E. Amid, and M.F. Imani. A fast method of steel surface defect detection using decision trees applied to lbp based features. In Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on, pages 1447–1452, July 2012. |
[47] | Sami M Halawani. A study of decision tree ensembles and feature selection for steel plates faults detection. |
[48] | A. Deloncle, R. Berk, F. D’Andrea, and M. Ghil. Weather regime prediction using statistical learning. Journal of the Atmospheric Sciences, 64(5):1619–1635, May 2007. |
[49] | A. Berrado and A. Rassili. Modeling and characterizing of the thixoforming of steel process parameters â€“ the case of forming load. International Journal of Material Forming, 3(1):735–738, 2010. |
[50] | Ren Ye Suganthan P.N. Laha, Dipak. Modeling of steelmaking process with effective machine learning techniques. Expert Systems With Applications,42(10):4687–4696, 2015. |
[51] | Lidia Auret and Chris Aldrich. Interpretation of nonlinear relationships between process variables by use of random forests. Minerals Engineering, 35:27 – 42, 2012. |
[52] | S.B. Kotsiantis, G.E. Tsekouras, and P.E. Pintelas. Bagging random trees for estimation of tissue softness. In Petra Perner and Atsushi Imiya, editors, Machine Learning and Data Mining in Pattern Recognition, volume 3587 of Lecture Notes in Computer Science, pages 674–681. Springer Berlin Heidelberg, 2005. |