Bug is an unavoidable problem in the development of all software systems. For developers of software system, bug report is a powerful tool for fixing bugs. However, manual recognition on bug reports tends to be time-consuming and not economical. It thus becomes significant to advance the automated classification approach to provide clear guidelines on how to assign a reasonable severity to a reported bug. In this study, several algrithoms are proposed based on extreme learning machine to automatically classify bug reports. Concretely, this study focuses on three problems in the field of bug report classification. The first one is the imbalanced class distribution in bug report dataset; the second is the insufficient labeled sample in bug report dataset; the last is the limited training data available. In order to solve these issues, three methods are proposed based on cost-sensitive supervised classification, semi-supervised learning, and sample transferring, respectively. Extensive experiments on real bug report datasets are conducted, and the results demonstrate the practicability and effectiveness of the proposed methods.