Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Floating search methods in feature selection
Pattern Recognition Letters
Neural networks for pattern recognition
Neural networks for pattern recognition
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection within a Simulated Annealing DataMining Algorithm
Journal of Intelligent Information Systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Comparison of Classifier-Specific Feature Selection Algorithms
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ImprovingWriter Identification by Means of Feature Selection and Extraction
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
| Hi-index | 0.00 |
This work tackles the problem of selecting a subset of featuresin an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm makes uses of a specially designed form of simulated annealingto find a subset of attributes that maximizes the objective function. The new algorithm is evaluated against one of the most widespread and reliable algorithms, the Sequential Forward Floating Search (SFFS). Our experimental results in classification tasks show that TFS achieves significant improvements over SFFS in the objective function with a notable reduction in subset size.