In this paper a diagnostic model based on Naive Bayes developed to diagnose erytemato squamous diseases. The hybrid feature selection method, named IGSBFS (Information Gain and Sequential Backward Floating Search), combines the advantages of filters and wrappers to select the optimal feature subset from the original feature set. In IGSBFS, Information Gain acts as filters to remove redundant features and SBFS with Naïve Bayes acts as the wrappers to select the ideal feature subset from the remaining features We conducted experiments in WEKA with 10 fold cross validation. The algorithm selected an optimum feature subset of 10 features with 98.9% accuracy.

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 classifi er with Gist-SVM. We enlarge the number of training examples with SVM-Naive Bayes classifi ers. We used WBC dataset from UCI Machine learning depository for our proposed methodology.

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