Floating search methods in feature selection
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Wrappers for feature subset selection
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LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A model for temperature prediction of melted steel in the electric arc furnace (EAF)
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Temperature prediction in electric arc furnace with neural network tree
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Evolutionary optimization of regression model ensembles in steel-making process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Neural network committees optimized with evolutionary methods for steel temperature control
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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Time reduction of steel scraps meltdown during the electic arc process is really a challenging problem. Typically the EAF process is stochastic without any determinism and only simple and naive rules are currently used to manage such processes. The goal of the paper is to present the way, which have been considered, to build an accurate model concerning different feature selection methods that would be helpful in predicting the end of the meltdown and maximum energy needed by the furnace.