Data This paper presents a novel classification scheme to improve classification performance when few training data are available. In the proposed scheme, data are described using the discretized statistical features. Solving the classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated data. It investigates an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world data sets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing classification methods.