FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Adaptive branch and bound algorithm for selecting optimal features
Pattern Recognition Letters
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
The search for optimal feature set in power quality event classification
Expert Systems with Applications: An International Journal
An improvement on floating search algorithms for feature subset selection
Pattern Recognition
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
The role of eigenvalues in linear feature selection theory
Journal of Computational and Applied Mathematics
Information Sciences: an International Journal
Multi-objective particle swarm optimisation (PSO) for feature selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Harmony-based feature selection to improve the nearest neighbor classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A genetic programming approach to hyper-heuristic feature selection
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A multi-objective feature selection approach based on binary PSO and rough set theory
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Novel initialisation and updating mechanisms in PSO for feature selection in classification
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
ACSC '12 Proceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 122
Growing Seed Genes from Time Series Data and Thresholded Boolean Networks with Perturbation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In the type of recognition system under discussion, the physical sample to be recognized is first subjected to a battery of tests; on the basis of the test results, the sample is then assigned to one of a number of prespecified categories. The theory of how test results should be combined to yield an optimal assignment has been discussed in an earlier paper. Here, attention is focused on the tests themselves. At present, we usually measure the effectiveness of a set of tests empirically, i.e., by determining the percentage of correct recognitions made by some recognition device which uses these tests. In this paper, we discuss some of the theoretical problems encountered in trying to determine a more formal measure of the effectiveness of a set of tests; a measure which might be a practical substitute for the empirical evaluation. Specifically, the following question is considered: What constitutes an effective set of tests, and how is this effectiveness dependent on the correlations among, and the properties of, the individual tests in the set? Specific suggestions are considered for the case in which the test results are normally distributed, but arbitrarily correlated. The discussion is supported by the results of experiments dealing with automatic recognition of hand-printed characters.