In this paper, we propose classifier combination based on active learning, which deals with the design of classifier combination systems as training a combiner at the aggregation level and introduces SVM active learning into the design of this multi-category decision combiner. This algorithm greatly reduces the number of labeled data the classifier system needs in order to achieve satisfactory performance. This algorithm consists of two main steps: firstly, designing and training first level classifiers which can output posterior probability vectors as the input of the second level combiner, secondly, designing second level combiner based on SVM active learning and classifying testing samples with this combiner. Experiments on standard database show that our algorithm performs better than current classifier combination rules when considering both labeling cost and classification accuracy.