NAVE BAYESIAN CLASSIFIER WITH FOLEY-SAMMON TRANSFORM
Zhenzhou Chen · Journal of South China Normal University · 2011
As an important classifying method in machine learning,Nave Bayesian classifier is based on the assumption that the attribute values are conditionally independent with given target values.According to this assumption,a Nave Bayesian classifier with Foley-Sammon Transform NBFST is proposed.The NBFST is compared with NB(Nave Bayesian),NBPCA(Nave Bayesian with principle component analysis) and NBFDA(Nave Bayesian with Fisher Discriminant Analysis) by experiments.Experiment results show that NBFST has higher accuracy than other classifying methods in most data sets.