The classification of imbalanced datasets has been recognized as a vital issue in the field of machine learning. In an imbalanced dataset, there are obviously fewer training examples of the minority class compared to the majority class so that the result of classification may be biased towards the latter. As a result, the classification performance of whole dataset has a tendency to be poor. Facing on the problem, an enhanced probability algorithm based on the Gaussian mixture model-expectation maximization(GMM-EM) method is proposed for imbalanced datasets. Firstly, the probability density functions(PDFS) of the minority class are obtained by using GMM and EM algorithms. Secondly, because original samples with high probability density have more powerful ability to generate new instances than low probability density samples according to the basic rule of probability theory, an enhanced probability algorithm is given based on PDF of the minority class. The algorithm ensures that the PDFs of the new balanced minority class are in accordance with the original minority class, and makes the minority class balanced in the sense of statistics. Finally, the proposed algorithm and other methods are applied together with a decision tree classifier for assessment. By choosing eight imbalanced datasets from UCI and KEEL repositories, experimental results show that the proposed algorithm is more effective than other methods.