Floating search methods in feature selection
Pattern Recognition Letters
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Feature selection in supervised and unsupervised learning via evolutionary search
Feature selection in supervised and unsupervised learning via evolutionary search
An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary Computation
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A multilevel tabu search algorithm for the feature selection problem in biomedical data
Computers & Mathematics with Applications
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
The Journal of Machine Learning Research
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Variable selection in the kernel regression based short-term load forecasting model
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Tournament feature selection with directed mutations
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
| Hi-index | 0.00 |
A new search method to the feature selection problem - the tournament searching - is proposed and compared with other popular feature selection methods. The tournament feature selection method is a simple stochastic searching method with only one parameter controlling the global-local searching properties of the algorithm. It is less complicated and easier to use than other stochastic methods, e.g. the simulated annealing or genetic algorithm. The algorithm was tested on several tasks of the feature selection in the supervised learning. For comparison the simulated annealing, genetic algorithm, random search and two deterministic methods were tested as well. The experiments showed the best results for the tournament feature selection method in relation to other tested methods.