A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Biological Cybernetics
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Advances in knowledge discovery and data mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimizing Neural Networks Using FasterMore Accurate Genetic Search
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Feature Selection for Counterpropagation Networks
Genetic Algorithms for Feature Selection for Counterpropagation Networks
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining and Knowledge Discovery
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Avoiding Pitfalls in Neural Network Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolution of functional link networks
IEEE Transactions on Evolutionary Computation
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification
Journal of Systems and Software
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ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
International Journal of Business Intelligence and Data Mining
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In this paper, an adequate set of input features is selected for functional expansion genetically for the purpose of solving the problem of classification in data mining using functional link neural network. The proposed method named as HFLNN aims to choose an optimal subset of input features by eliminating features with little or no predictive information and designs a more compact classifier. With an adequate set of basis functions, HFLNN overcomes the non-linearity of problems, which is a common phenomenon in single layer neural networks. The properties like simplicity of the architecture (i.e., no hidden layer) and the low computational complexity of the network (i.e., less number of weights to be learned) encourage us to use it in classification task of data mining. We present a mathematical analysis of the stability and convergence of the proposed method. Further the issue of statistical tests for comparison of algorithms on multiple datasets, which is even more essential in data mining studies, has been all but ignored. In this paper, we recommend a set of simple, yet safe, robust and non-parametric tests for statistical comparisons of the HFLNN with functional link neural network (FLNN) and radial basis function network (RBFN) classifiers over multiple datasets by an extensive set of simulation studies.