Dynamic Feature Selection with Wrapper Model and Ensemble Approach based on Measures of Local Relevances and Group Diversity using Genetic Algorithm

Marek Kurzynski, Pawel Trajdos, Maciej Krysmann

2015

Abstract

In the paper the novel feature selection method, using wrapper model and ensemble approach, is presented. In the proposed method features are selected dynamically, i.e. separately for each classified object. First, a set of identical one-feature classifiers using different single feature is created and next the ensemble of features (classifiers) is selected as a solution of optimization problem using genetic algorithm. As an optimality criterion, the sum of measures of features relevance and diversity of ensemble of features is adopted. Both measures are calculated using original concept of randomized reference classifier, which on average acts like classifier with evaluated feature. The performance of the proposed method was compared against six state-of- art feature selection methods using nine benchmark databases. The experimental results clearly show the effectiveness of the dynamic mode and ensemble approach in feature selection procedure.

Paper Citation

in Harvard Style

Kurzynski M., Trajdos P. and Krysmann M. (2015). Dynamic Feature Selection with Wrapper Model and Ensemble Approach based on Measures of Local Relevances and Group Diversity using Genetic Algorithm . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 167-173. DOI: 10.5220/0005591401670173

in Bibtex Style

@conference{ecta15,
author={Marek Kurzynski and Pawel Trajdos and Maciej Krysmann},
title={Dynamic Feature Selection with Wrapper Model and Ensemble Approach based on Measures of Local Relevances and Group Diversity using Genetic Algorithm},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={167-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005591401670173},
isbn={978-989-758-157-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Dynamic Feature Selection with Wrapper Model and Ensemble Approach based on Measures of Local Relevances and Group Diversity using Genetic Algorithm
SN - 978-989-758-157-1
AU - Kurzynski M.
AU - Trajdos P.
AU - Krysmann M.
PY - 2015
SP - 167
EP - 173
DO - 10.5220/0005591401670173