uylaş sati, nur muğla sıtkı koçman university - bodrum vocational school of maritime, Muğla, Turkey

collective learning approach for semi supervised data classification

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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.

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Semi , Supervised data classification , Clustering method , Supervised data classification , Machine learning , Mathematical programming

Pamukkale University Journal Of Engineering Sciences

869