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Title: Feature selection for MLP neural network: The use of random permutation of probabilistic outputs
Authors: Yang, J.-B.
Shen, K.-Q. 
Ong, C.-J. 
Li, X.-P. 
Keywords: Feature ranking
Feature selection
Multilayer perceptrons (MLPs)
Probabilistic outputs
Random permutation
Issue Date: Dec-2009
Citation: Yang, J.-B., Shen, K.-Q., Ong, C.-J., Li, X.-P. (2009-12). Feature selection for MLP neural network: The use of random permutation of probabilistic outputs. IEEE Transactions on Neural Networks 20 (12) : 1911-1922. ScholarBank@NUS Repository.
Abstract: This paper presents a new wrapper-based feature selection method for multilayer perceptron (MLP) neural networks. It uses a feature ranking criterion to measure the importance of a feature by computing the aggregate difference, over the feature space, of the probabilistic outputs of the MLP with and without the feature. Thus, a score of importance with respect to every feature can be provided using this criterion. Based on the numerical experiments on several artificial and real-world data sets, the proposed method performs, in general, better than several selected feature selection methods for MLP, particularly when the data set is sparse or has many redundant features. In addition, as a wrapper-based approach, the computational cost for the proposed method is modest. © 2009 IEEE.
Source Title: IEEE Transactions on Neural Networks
ISSN: 10459227
DOI: 10.1109/TNN.2009.2032543
Appears in Collections:Staff Publications

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