Selective Bayesian Classifier Based on Semi-supervised Clustering

Small scale of labeled samples results in incorrect of computation of mutual information, which may lower the classification accuracy of minimal-redundancymaximal-relevance (mRMR) selective Bayesian classifiers. In order to solve the above problem, a kind of selective Bayesian classifier based on semi-supervised clustering algorithm is proposed. At first, a new semi-supervised Krepresentative clustering algorithm is designed by using the Bayesian posterior probability, which is applied to labeling the unlabeled samples so as to enlarge the scale of labeled samples. Then a novel feature selection criterion is proposed by combining the mRMR and the concept of Markov blanket to automatically determine a reasonably compact subset of features. In addition, a risk-regulation factor is introduced into the feature selection criterion to reduce the risk of mislabeling. At last, a Bayesian classifier is constructed based on the preprocessed samples. Experimental results indicate that the proposed Bayesian classifier can select optimal features to obtain high classification accuracy.