Bi-objective genetic algorithm for feature selection in ensemble systems

Authors:
Laura E. A. Santana;Anne M. P. Canuto
Affiliations:
Informatics and Applied Mathematics Department, Federal University of RN Natal, RN, Brazil;Informatics and Applied Mathematics Department, Federal University of RN Natal, RN, Brazil
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 5
Cited 0

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
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles

Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the use of a bi-objective genetic algorithm to select attributes for an ensemble system. This is achieved by using this technique to simultaneously maximize the individual diversity of the base classifiers and the group diversity of an ensemble system. In order to evaluate the possible solutions obtained by this technique, two filter-based evaluation criteria will be used. Filter-based criteria were chosen because they are independent of the learning algorithm and have a low computational cost.