Decision Varied from Entropy to Parametric Distribution
从熵均值决策到样本分布决策

In order to improve the predictive accuracy of inductive learning, a heavy analysis about the demerit of C4.5 is given, and the reason why there are many debates and compromise between standard method and meta algorithms is pointed out. By the method of estimating the probability distribution of training examples, a new and simple method of decision tree is turned out. Experimental results on UCI data sets show that the proposed method has good performance on accuracy issue and faster computing speed than C4.5 algorithm.