Classification algorithm for self-learning Naive Bayes based on conditional information entropy
基于条件信息熵的自主式朴素贝叶斯分类算法

Nave Bayes algorithm is an effective simple classification algorithm.But two central assumptions made by the Nave Bayes approach are that the attributes are independent within each class and the importance of the attributes is equal,which can harm the classification process to some extent.It is a very difficult problem in machine learning to carry out self-learning knowledge according to the characteristic of source data without prior domain knowledge.Based on the theory of rough set,a new Nave Bayes method named Conditional Information Entropy-based Algorithm for Self-learning Nave Bayes(CIEBASLNB)was proposed,which combined the merits of selective Nave Bayes(SNB)and Weighted Nave Bayes(WNB).Simulation results on a variety of UCI data sets illustrate the efficiency of this method.