Genetic algorithms as a pre processing strategy for imbalanced datasets

Authors:
Marcelo Beckmann;Beatriz Souza L.P. de Lima;Nelson F.F. Ebecken
Affiliations:
Federal University of Rio de Janeiro, Rio de Janeiro, Brazil;Federal University of Rio de Janeiro, Rio de Janeiro, Brazil;Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

In data mining, the traditional classification algorithms tend to loose its predictive capacity when applied on a dataset which distribution between classes is imbalanced. This work aims to present a new methodology using genetic algorithms, in order to create synthetic instances from the minority class. The experiments with the proposed methodology demonstrated a better classification performance in most of the problems, in comparison with other work in the specific literature.