A novel semi-supervised learning for SMS classification


In this paper, we propose a novel semi-supervised methodology to detect spam or ham SMSs, using frequent item set mining algorithm Apriori, probabilistic model Naive Bayes and ensemble learning. This paper considers the unbalanced data set problem which means designing of two class SMS classifier using small number of ham and unlabeled dataset only. Using… (More)
DOI: 10.1109/ICMLC.2014.7009721


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