In typical data mining applications, labeling the large amounts of data is difficult, expensive, and time consuming, if annotated manually.To avoid manual labeling, semi-supervised learninguses unlabeled data along withthe labeled data in the training process. Transductive support vector machine (TSVM) is one such semi-supervised, which has been found effective in enhancing the classification performance. However there are some deficiencies in TSVM, such as presetting number of the positive

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... s samples, frequently exchange of class label, and its requirement for larger amount of unlabeled data. To tackle these deficiencies, in this paper, we propose a newsemi-supervised learning algorithm based on active learning (AL) combined with TSVM. The algorithm applies active learning to select the most informative instances based on the version space minimum-maximum division with human annotation for improve the classification performance.Simultaneously, in order to make full use of the distributioncharacteristics of unlabeled data, we addeda manifold regularization term to the objective function.Experiments performed on severalUCI datasetsdemonstrate that our proposedmethod achieves significant improvementoverother benchmark methods yet consuming less amount of human effort, which is very important while labeling data manually.