A large project (e.g., Ubuntu) usually contains a large number of software packages. Sometimes the same bug report in such project would affect multiple packages, and developers of different packages need to collaborate with one another to fix the bug. Unfortunately, the total number of packages involved in a project like Ubuntu is relatively large, which makes it time-consuming to manually identify packages that are affected by a bug report. In this paper, we propose an approach named PkgRec that consists of 2 components: a name matching component and an ensemble learning component. In the name matching component, we assign a confidence score for a package if it is mentioned by a bug report. In the ensemble learning component, we divide the training dataset into n subsets and build a sub-classifier on each subset. Then we automatically determine an appropriate weight for each sub-classifier and combine them to predict the confidence score of a package being affected by a new bug report. Finally, PkgRec combines the name matching component and the ensemble learning component to assign a final confidence score to each potential package. A list of top-k packages with the highest confidence scores would then be recommended. We evaluate PkgRec on 3 datasets including Ubuntu, OpenStack, and GNOME with a total number of 42,094 bug reports. We show that PkgRec could achieve recall@5 and recall@10 scores of 0.511-0.737, and 0.614-0.785, respectively. We also compare PkgRec with other state-of-art approaches, namely LDA-KL and MLkNN. The experiment results show that PkgRec on average improves recall@5 and recall@10 scores of LDA-KL by 47% and 31%, and MLkNN by 52% and 37%, respectively.