Naive Bayesian classifier with Foley-Sammon Transform

As an important classifying method in machine learning, Naive Bayesian classifier is based on the assumption that the attribute values are conditionally independent given the target value. According to the assumption, a naive Bayesian classifier with Foley-Sammon Transform NBFST is proposed. The NBFST is compared with NB (Naive Bayesian), NBPCA (Naive Bayesian with principle component analysis) and NBFDA (Naive Bayesian with Fisher Discriminant Analysis) by an experiment. Experiment results show that NBFST has higher accuracy in most datasets.