Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A practical Bayesian framework for backpropagation networks
Neural Computation
Network generalization differences quantified
Neural Networks
Bayesian methods for supervised neural networks
The handbook of brain theory and neural networks
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Engineering multiversion neural-net systems
Neural Computation
Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Quantifying Relevance of Input Features
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Feature salience definition and estimation and its use in feature subset selection
Intelligent Data Analysis
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
Inelligent ensemble system aids osteoporosis early detection
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Mutual information based input feature selection for classification problems
Decision Support Systems
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We assess the relative merits of a number of techniques designed to determine the relative salience of the elements of a feature set with respect to their ability to predict a category outcome—for example, which features of a character contribute most to accurate character recognition. A number of different neural-net-based techniques have been proposed (by us and others) in addition to a standard statistical technique, and we add a technique based on inductively generated decision trees. The salience of the features that compose a proposed set is an important problem to solve efficiently and effectively, not only for neural computing technology but also in order to provide a sound basis for any attempt to design an optimal computational system. The focus of this study is the efficiency and the effectiveness with which high-salience subsets of features can be identified in the context of ill-understood and potentially noisy real-world data. Our two simple approaches, weight clamping using a neural network and feature ranking using a decision tree, generally provide a good, consistent ordering of features. In addition, linear correlation often works well.