Incremental Bayesian classification for multivariate normal distribution data
Bayesian classifier is an effective and fundamental methodology for solving classification problems. However, it is computationally efficient when all features are considered simultaneously. But sometimes all the features do not contribute significantly to classification. Also the noisy attributes sometimes may decrease the accuracy of classifier. So before classification feature selection is used as a pre-processing step. When the features are added one by one in Bayesian classifier in batch mode in forward selection method huge computation is involved. In this paper, an incremental Bayesian classifier for multivariate normal distribution datasets are proposed. The proposed incremental Bayesian classifier is computationally efficient over batch Bayesian classifier in terms of time. The effectiveness of the proposed incremental Bayesian classifier has been demonstrated through experiments on different datasets. It is found on the basis of experiments that the incremental Bayesian classifier has an equivalent power compared to batch Bayesian classifier in terms classification accuracy. However, the proposed incremental Bayesian classifier has very high speed efficiency in comparison to batch Bayesian classifier.
bayesian classification multivariate normal distribution feature selection sequential classification