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|Title:||Feature selection via sensitivity analysis of MLP probabilistic outputs|
|Citation:||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|
|Appears in Collections:||Staff Publications|
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