Feature subset selection in a methodology for training and improving artificial neural network weights and connections. Zanchettin, C. & Ludermir, T. In Proceedings of the International Joint Conference on Neural Networks, 2008. doi abstract bibtex This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of Multi-Layer Perceptron neural networks. We compare the performance of the proposed method for feature subset selection to five classical feature selection methods in three different classification problems. © 2008 IEEE.
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title = {Feature subset selection in a methodology for training and improving artificial neural network weights and connections},
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year = {2008},
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abstract = {This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of Multi-Layer Perceptron neural networks. We compare the performance of the proposed method for feature subset selection to five classical feature selection methods in three different classification problems. © 2008 IEEE.},
bibtype = {inproceedings},
author = {Zanchettin, C. and Ludermir, T.B.},
doi = {10.1109/IJCNN.2008.4634065},
booktitle = {Proceedings of the International Joint Conference on Neural Networks}
}
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