朴素贝叶斯分类器在机器学习领域中一种重要的分类算法，但是该算法的前提是：要求数据集在给出分类属性的情况下，其他属性之间是独立的。根据这个前提，利用Foley-Sammon变换算法进行特征提取，提出了一种基于Foley-Sammon变换的朴素贝叶斯分类器NBFST(Naive Bayesian classifier with Foley-Sammon Transform)。实验表明，NBFST能够在大多数数据集上具有较高的分类准确率。
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.