An empirical study on ensemble selection for class-imbalance data sets

The algorithm of GASEN (Genetic Algorithm based Selective Ensemble Network) has been proven to be a very effective way to select a subset of neural networks to form an ensemble classifier or a regressor of enhanced generation ability. And yet performance of GASEN on class-imbalance data sets hasn´t been discussed widely, while class-imbalance learning itself is an increasingly important issue. In this paper, an improved solution of GASEN is proposed to handle this kind of problem where research achievements from class-imbalance learning field is employed.