Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Advances in Feature Selection with Mutual Information
Similarity-Based Clustering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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In the context of feature selection, there is a trade-off between the number of selected features and the generalisation error. Two plots may help to summarise feature selection: the feature selection path and the sparsity-error trade-off curve. The feature selection path shows the best feature subset for each subset size, whereas the sparsity-error trade-off curve shows the corresponding generalisation errors. These graphical tools may help experts to choose suitable feature subsets and extract useful domain knowledge. In order to obtain these tools, extreme learning machines are used here, since they are fast to train and an estimate of their generalisation error can easily be obtained using the PRESS statistics. An algorithm is introduced, which adds an additional layer to standard extreme learning machines in order to optimise the subset of selected features. Experimental results illustrate the quality of the presented method.