The ensemble is a technique that strategically combines basic models to achieve better accuracy rates.Diversity,combination methods,and selection topology are the main factors determining ensemble performance.Conse-quently,it is a challenging task to design an efficient ensemble scheme.Even though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for improvement.This paper proposes a general framework for creating ensembles in the context of classification.Specifically,the ensemble framework consists of four stages:objectives,data preparing,model training,and model testing.It is comprehensive to design diverse ensembles.The proposed ensemble approach can be used for a wide variety of machine learning tasks.We validate our approach on real-world datasets.The experimental results show the efficiency of the proposed approach.