It is well-known that in order to build a strong ensemble, the component leers should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component leers constructed, then Bagging can effectively improve accuracy. However, for stable leers such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component leers, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component leers, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.