Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature selection with neural networks
Pattern Recognition Letters
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A GA-based RBF classifier with class-dependent features
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural-network feature selector
IEEE Transactions on Neural Networks
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
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Double parallel feedforward neural network (DPFNN) based approach is proposed for dimensionality reduction, which is one of very significant problems in multi- and hyperspectral image processing and is of high potential value in lunar and Mars exploration, new earth observation system, and biomedical engineering etc. Instead of using sequential search like most feature selection methods based on neural network (NN), the new approach adopts feature weighting strategy to cut down the computational cost significantly. DPFNN is trained by a mean square error function with regulation terms which can improve the generation performance and classification accuracy. Four experiments are carried out to assesses the performance of DPFNN selector for high-dimensional data classification. The first three experiments with the benchmark data sets are designed to make comparison between DPFNN selector and some NN based selectors. In the fourth experiment, hyperspectral data, that is an airborne visible/infrared imaging spectrometer (AVIRIS) data set, is used to compare DPFNN selector with widely used forward sequential search methods using the Maximum Likelihood classifier (MLC) as criterion. Experiments show the effectiveness of the new feature selection method based on DPFNNs.