  One of the most significant current discussions in the field of data mining
is classifying imbalanced data. In recent years, several ways are proposed such
as algorithm level (internal) approaches, data level (external) techniques, and
cost-sensitive methods. Although extensive research has been carried out on
imbalanced data classification, however, several unsolved challenges remain
such as no attention to the importance of samples to balance, determine the
appropriate number of classifiers, and no optimization of classifiers in the
combination of classifiers. The purpose of this paper is to improve the
efficiency of the ensemble method in the sampling of training data sets,
especially in the minority class, and to determine better basic classifiers for
combining classifiers than existing methods. We proposed a hybrid ensemble
algorithm based on Genetic Programming (GP) for two classes of imbalanced data
classification. In this study uses historical data from UCI Machine Learning
Repository to assess minority classes in imbalanced datasets. The performance
of our proposed algorithm is evaluated by Rapid-miner studio v.7.5.
Experimental results show the performance of the proposed method on the
specified data sets in the size of the training set shows 40% and 50% better
accuracy than other dimensions of the minority class prediction.
