ACM SIGNUM Newsletter
Sensitivity analysis in linear regression
Sensitivity analysis in linear regression
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Neural Processing Letters
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Quasi-regression with shrinkage
Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
An introduction to variable and feature selection
The Journal of Machine Learning Research
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Feature Selection Based on Sensitivity Analysis
Current Topics in Artificial Intelligence
Sensitivity analysis of spatial models
International Journal of Geographical Information Science
A Wrapper Method for Feature Selection in Multiple Classes Datasets
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
An improved version of the wrapper feature selection method based on functional decomposition
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Combining functional networks and sensitivity analysis as wrapper method for feature selection
Expert Systems with Applications: An International Journal
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A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples.