Convergence of an annealing algorithm
Mathematical Programming: Series A and B
Feature selection for automatic classification of non-Gaussian data
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Applying statistical knowledge to database analysis and knowledge base construction
Proceedings of the sixth conference on Artificial intelligence applications
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A Mathematical Theory of Communication
A Mathematical Theory of Communication
LESS: A Model-Based Classifier for Sparse Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Consensus unsupervised feature ranking from multiple views
Pattern Recognition Letters
A Thermodynamical Search Algorithm for Feature Subset Selection
Neural Information Processing
Information Sciences: an International Journal
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An overview of the principle feature subset selection methods isgiven. We investigate a number of measures of feature subset quality, usinglarge commercial databases. We develop an entropic measure, based upon theinformation gain approach used within ID3 and C4.5 to build trees, which isshown to give the best performance over our databases. This measure is usedwithin a simple feature subset selection algorithm and the technique is usedto generate subsets of high quality features from the databases. A simulatedannealing based data mining technique is presented and applied to thedatabases. The performance using all features is compared to that achievedusing the subset selected by our algorithm. We show that a substantialreduction in the number of features may be achieved together with animprovement in the performance of our data mining system. We also present amodification of the data mining algorithm, which allows it to simultaneouslysearch for promising feature subsets and high quality rules. The effect ofvarying the generality level of the desired pattern is alsoinvestigated.