Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICSMC.2008.4811372
Title: Feature selection via sensitivity analysis of MLP probabilistic outputs
Authors: Yang, J.-B.
Shen, K.-Q. 
Ong, C.-J. 
Li, X.-P. 
Keywords: Feature raking
Feature selection
Multi-layer perceptrons
Probabilistic outputs
Random permutation
Issue Date: 2008
Source: Yang, J.-B., Shen, K.-Q., Ong, C.-J., Li, X.-P. (2008). Feature selection via sensitivity analysis of MLP probabilistic outputs. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics : 774-779. ScholarBank@NUS Repository. https://doi.org/10.1109/ICSMC.2008.4811372
Abstract: This paper presents a new wrapper-based feature selection method for multi-layer perceptrons (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. The proposed criterion has inexpensive evaluation. Based on the numerical experiment on several artificial and real-world data sets, the proposed method performs at least as well, if not better, than several existing feature selection methods for MLP. © 2008 IEEE.
Source Title: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/73467
ISSN: 1062922X
DOI: 10.1109/ICSMC.2008.4811372
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