In this paper, we consider the problem of how to alleviate the conditional independence assumption of naive Bayes. We try to find an equivalent set of variables for the attributes of the class such that these variables are nearly conditionally independent. For the case that all attributes are continuous variables, we put forward the theory of class-weighting supervised principal component analysis (CWSPCA) to improve naive Bayes. For the categorical case, we construct the equivalent variables by rearranging the values of the attributes, and propose the decremental association rearrangement (DAR) algorithm and its multiple version (MDAR). Finally, we make a benchmarking study to show the performance of our methods. The experimental results reveal that naive Bayes can be greatly improved by means of properly transforming the original attributes.

在本文中，我们考虑如何减轻朴素贝叶斯的条件独立假设的问题。我们尝试为类的属性找到一组等效的变量，使得这些变量几乎是条件独立的。针对所有属性均为连续变量的情况，我们提出类加权监督主成分分析（ CWSPCA ）理论来改进朴素贝叶斯。对于分类情况，我们通过重新排列属性值来构造等效变量，并提出递减关联重排（DAR）算法及其多重版本（MDAR）。最后，我们进行了基准测试研究来展示我们方法的性能。实验结果表明，通过对原始属性进行适当的改造，可以极大地提高朴素贝叶斯的性能。