Mining Production Data with Neural Network & CART
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Lamb Meat Quality Assessment by Support Vector Machines
Neural Processing Letters
Analyzing currency crises' real effects with partial least squares sensitivity analysis
Intelligent Data Analysis
Rating organ failure via adverse events using data mining in the intensive care unit
Artificial Intelligence in Medicine
Identification of ischemic heart disease via machine learning analysis on magnetocardiograms
Computers in Biology and Medicine
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
Symbiotic Data Mining for Personalized Spam Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mortality assessment in intensive care units via adverse events using artificial neural networks
Artificial Intelligence in Medicine
Using Data Mining for Wine Quality Assessment
DS '09 Proceedings of the 12th International Conference on Discovery Science
Developing a product quality fault detection scheme
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Data mining with neural networks and support vector machines using the R/rminer tool
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Symbiotic filtering for spam email detection
Expert Systems with Applications: An International Journal
Using sensitivity analysis and visualization techniques to open black box data mining models
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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A novel neural network based technique, called “data strip mining” extracts predictive models from data sets which have a large number of potential inputs and comparatively few data points. This methodology uses neural network sensitivity analysis to determine which predictors are most significant in the problem. Neural network sensitivity analysis holds all but one input to a trained neural network constant while varying each input over its entire range to determine its effect on the output. Elimination of variables through neural network sensitivity analysis and predicting performance through model cross-validation allows the analyst to reduce the number of inputs and improve the model's predictive ability at the same time. This paper demonstrates its effectiveness on a pair of problems from combinatorial chemistry with over 400 potential inputs each. For these data sets, model selection by neural sensitivity analysis outperformed other variable selection methods including the forward selection and genetic algorithm