An improved maximum relevance and minimum redundancy selective Bayesian classifier

A kind of improved mRMR SBC was proposed by using K-means clustering and incremental learning algorithms to enlarge the scale of training samples. On one hand, the testing samples are labeled using the K-means clustering algorithm and are added to the training set. A regulatory factor is introduced into the process of attribute selection to reduce the risk of mislabel resulting from K-means clustering. On the other hand, some samples that are most helpful for improving the current classification accuracy are selected from the testing set and are added to the training set. Based on the enlarged training set, parameters in the Bayesian classifier are adjusted incrementally. Experimental results show that compared with mRMR SBC, the proposed Bayesian classifier has better classification results and is applicable for solving the classification problem for the high-dimensional dataset with little labels.