A Classification Method Based on Feature Selection for Imbalanced Data

Imbalanced data are very common in the real world, and it may deteriorate the performance of the conventional classification algorithms. In order to resolve the imbalanced classification problems, we propose an ensemble classification method that combines evolutionary under-sampling and feature selection. We employ the Bootstrap method in original data to generate many sample subsets. Read more