Wed, 20 Aug 2008 22:17:28 BST CiteULike: sona sensitivity-analysis http://www.citeulike.org/user/sona/tag/sensitivity-analysis CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/sona/article/2646542 <i>International Joint Conference on Neural Networks (IJCNN), Vol. 4 (2001), pp. 2478-2482 vol.4.</i><br /><br />This paper illustrates a new approach to sensitivity analysis for feature selection using multiple ensemble neural networks in a bootstrapping mode with bagging. This methodology is applied to in-silico drug design with QSAR (quantitative structural activity relationship), which is notoriously challenging for machine learning because typically there are on the order of 300-1000 dependent features, often for as few as 50-100 data points. For an HIV dataset with 160 wavelets descriptors, the number of relevant features was reduced to 35, and the resulting predictive neural network model gave better results than with the full feature set Bagging neural network sensitivity analysis for feature reduction for in-silico drug design MJ Embrechts F Arciniegas M Ozdemir CM Breneman K Bennett L Lockwood doi:10.1109/IJCNN.2001.938756 International Joint Conference on Neural Networks (IJCNN), Vol. 4 (2001), pp. 2478-2482 vol.4. 2008-04-09T17:00:11-00:00 2001 International Joint Conference on Neural Networks (IJCNN) 4 2478 2482 vol.4 dimensionality-reduction embedded-feature-selection feature-selection neural-networks sensitivity-analysis http://www.citeulike.org/user/sona/article/2626199 <i>Journal of Machine Learning Research, Vol. 7 (July 2006), pp. 1159-1182.</i><br /><br />This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers. First, random values are assigned to the outputs of the first layer; later, these initial values are updated based on sensitivity formulas, which use the weights in each of the layers; the process is repeated until convergence. Since these weights are learnt solving a linear system of equations, there is an important saving in computational time. The method also gives the local sensitivities of the least square errors with respect to input and output data, with no extra computational cost, because the necessary information becomes available without extra calculations. This method, called the Sensitivity-Based Linear Learning Method, can also be used to provide an initial set of weights, which significantly improves the behavior of other learning algorithms. The theoretical basis for the method is given and its performance is illustrated by its application to several examples in which it is compared with several learning algorithms and well known data sets. The results have shown a learning speed generally faster than other existing methods. In addition, it can be used as an initialization tool for other well known methods with significant improvements. A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis Enrique Castillo Bertha Guijarro-Berdiñas Oscar Fontenla-Romero Amparo Alonso-Betanzos Journal of Machine Learning Research, Vol. 7 (July 2006), pp. 1159-1182. 2008-04-03T14:18:05-00:00 2006 Journal of Machine Learning Research 7 1159 1182 fast-learning learning-algorithms neural-networks sensitivity-analysis supervised