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In data mining, the classification aims to label events and objects according
classes previously established. Nevertheless, the traditional classification algorithms
tend to loose its predictive capacity when applied on a dataset which distribution
between classes is imbalanced.
One of the strategies to resolve this problem is to execute a pre-processing on a
dataset in order to equalize the examples distribution among the classes.
This work aims to present one proposal of pre-processing using genetic
algorithms, in order to create synthetic instances from the class with less number of
instances. The experiments with the proposal algorithm demonstrated a better
classification performance in most of the problems, in comparison with three studies
published. It was also demonstrated the synthetic instances were created far from the
decision surface, and the application of incremental learning technique decreased the