An algorithm proposed for Semi-Supervised learning in cancer detection

Semi-supervised learning, a relatively new area in machine learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labelled data whenever only a small set of labelled examples is available. In this paper an algorithm for Semi Supervised learning for detecting Cancer is proposed. We use the few labelled data to train the SVM classifier with Gist-SVM. We enlarge the number of training examples with SVM-Naive Bayes classifiers. We used WBC dataset from UCI Machine learning depository for our proposed methodology.