The cover source mismatch problem in steganalysis is a serious problem which keeps current steganalysis from practical use. It is mainly because of the high intra-class variation of cover and stego samples in the feature space, since current ste-ganalytic features are inevitably affected much by the image content, size, quality and many other factors. Small training set often reflects only part of the real data distribution, hence the classifier (steganalyzer) may be undertrained and lack of robustness. In this paper, we propose a scheme to efficiently construct large representative training set for steganalysis. We also scheme out weighted ensemble classifiers which can be adaptive to testing data. Experimental results show that our method can improve the performance and robustness of ste-ganalysis under high intra-class variation.