A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Bi-Criterion Optimization with Multi Colony Ant Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Feature selection for ensembles applied to handwriting recognition
International Journal on Document Analysis and Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Optimization of Core Point Detection in Fingerprints
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
ReinSel: A class-based mechanism for feature selection in ensemble of classifiers
Applied Soft Computing
| Hi-index | 12.05 |
Feature selection methods select a subset of attributes (features) of a dataset and it is done based on a defined measure, eliminating the redundant and irrelevant ones. When a feature selection method is applied in a dataset, we aim to improve the quality of the dataset representation. For ensemble systems, feature selection techniques can supply different feature subsets for the individual components, reducing the redundancy that can exist among the features of an input pattern and to increase the diversity level of these systems. This paper proposes the application of three well-known optimization techniques (particle swarm optimization, ant-colony optimization and genetic algorithms), in both mono and bi-objective versions, to choose subsets of features for the individual components of ensembles. The feature selection process was based on two filter-based evaluation criteria that tried to capture the idea of diversity of individual classifiers and group diversity of an ensemble system. In this case, these optimization techniques try to maximize these diversities measures, either individually (mono-objective) or together (bi-objective). An empirical analysis was performed, where all ensemble systems were applied to 11 datasets and we compared both mono and bi-objective versions among each other and with a random subset procedure. Based on the empirical analysis, we will observe that PSO with a bi-objective function will be the most promising direction, when selecting attributes for individual components of ensemble systems.