collective learning approach for semi supervised data classification

Semi-supervised data classification is one of significant field of study in machine learning and data mining since it deals with datasets which consists both a few labeled and many unlabeled data. the researchers have interest in this field because in real life most of the datasets have this feature. in this paper we suggest a collective method for solving semi-supervised data classification problems. examples in r1 presented and solved to gain a clear understanding. for comparison between state of art methods, well-known machine learning tool weka is used. experiments are made on real-world datasets provided in uci dataset repository. results are shown in tables in terms of testing accuracies by use of ten fold cross validation.