Ensemble methods are commonly used for enhancing robustness in machine learning. However, due to the ''transferability'' of adversarial examples, the performance of an ensemble model can be seriously affected even it contains a set of independently trained sub-models. To address this issue, we propose an efficient data transformation method based on a cute ''weakness allocation'' strategy, to diversify non-robust features.Our approach relies on a fine-grained analysis on the relation between non-robust features and adversarial attack directions.Moreover, our approach enjoys several other advantages, e.g., it does not require any communication between sub-models and the construction complexity is also quite low.We conduct a set of experiments to evaluate the performance of our proposed method and compare it with several popular baselines. The results suggest that our approach can achieve significantly improved robust accuracy over most existing ensemble methods, and meanwhile preserve high clean accuracy.