Although the anomaly detection problem has been widely studied in data mining and machine learning, most algorithms in this domain have been performed with limited generalization ability. To that end, ensemble learning has been proven to effectively improve the generalization ability of anomaly detection algorithms. However, there is room for further improvement in existing anomaly ensemble methods. For example, these methods are based on a single-level ensemble strategy that only considers the combination of the final results and usually neglects the loss of information during the generation of multiple subspaces. In this paper, we propose a double-level ensemble learning method using linear regression as the base detector called DELR, which has better robustness and can reduce the risk of information loss. The first level is used to reduce the loss of information, and the second level is used to improve the generalization ability. To better satisfy the diversity requirement for the anomaly ensemble, we present a diversity loss function to retrain the base models. Furthermore, we devise a novel weighted average strategy to ensure effectiveness in the second level. Our experimental results and analysis demonstrate that the DELR algorithm obtains better generalization ability over real-world datasets compared to several state-of-art anomaly algorithms.