Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Feature selection for automatic classification of non-Gaussian data
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Recognition Letters
A novel prostate cancer classification technique using intermediate memory tabu search
EURASIP Journal on Applied Signal Processing
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Generalized multiscale radial basis function networks
Neural Networks
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
Time space tradeoffs in GA based feature selection for workload characterization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Information Sciences: an International Journal
A system for classification of time-series data from industrial non-destructive device
Engineering Applications of Artificial Intelligence
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Feature selection is a dimensionality reduction problem in order to reduce measurement costs, shorten computational time, relieve the curse of dimensionality, and improve classification accuracy. In this paper, a hybrid approach using tabu search and probabilistic neural networks is proposed and applied to feature selection problems. The proposed tabu search algorithm differs from previous research by using a long-term memory instead of a short-term memory to avoid the necessity of the delicate tuning of the memory length and to decrease the risk of generating a cycle that traps the search in local optimal solutions. The probabilistic neural networks integrated in the proposed hybrid approach are an outgrowth of Bayesian classifiers that outperform backpropagation-based neural networks in their global convergence and rapid training. Extensive experiments on real-world data sets are performed and the comparison with previous research indicates that the proposed hybrid approach can select an equal or smaller number of features while improving classification accuracy.