Please use this identifier to cite or link to this item:
|Title:||Feature selection via sensitivity analysis of MLP probabilistic outputs|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.