Feature selection and ensemble learning can be used to improve the accuracy and robustness of epileptic seizure detection and classification. Unfortunately, a few studies have fully utilized feature selection and ensemble learning. In this paper, we present an adaptive hybrid feature selection-based classifier ensemble (AHFSE) for epileptic seizure classification. The AHFSE creates new sample subsets in every bootstrap using adaptive hybrid feature selection. It combines them using rank aggregation to obtain a distinguished subset of features. These new samples subsets are then fed into a classifier. Finally, majority voting is used to complete the detection and classification tasks. The AHFSE is designed to obtain an optimized subset of features based on the different samples in every bootstrap, which have a tendency to generate different results with respect to rank aggregation. With discrete wavelet transform, the experiments based on binary and multi-class tasks show that the AHFSE performs well on the Bonn data set and improves the specificity, sensitivity, or accuracy of the selected features by combining the subsets of different feature selections to obtain new samples within the bagging process. Furthermore, the adaptive process helps the framework obtain the optimum combination of the feature selection algorithm. The AHFSE also obtains more desirable final results in several perspectives, such as: 1) compared with other feature selection methods; 2) compared with other ensemble methods; and 3) compared with other research that uses discrete wavelet transform as a preprocessing step.