基于分类矩阵的决策树算法

为了提高决策树分类的速度和精确率,提出了一种基于分类矩阵的决策树算法.介绍了ID3算法的理论基础,定义了一种分类矩阵,指出了ID3算法的取值偏向性并利用分类矩阵给出了证明.在此基础上,引入了一个权重因子,抑制了原有算法的取值偏向,并利用分类矩阵给出相应证明,同时根据基于分类矩阵增益的特点,提出了新的决策树分类方案,旨在运算速率上进行优化,与原有算法进行了实验比较.对实验结果分析表明,优化后的方案在性能上有明显改善.

Decision tree algorithm based on classification matrix

To improve the classification speed and accuracy of the decision tree algorithm,a new program is proposed based on classification matrix.Firstly,the basic theory of the ID3 algorithm is introduced and a classification matrix is defined.Then the variety bias of this algorithm is pointed out,which is proved using the classification matrix.On the basis of the above,a weighting factor is cited to suppress the variety bias of the ID3 algorithm on the premise of a corresponding proof.According to the characteristics of the gain based on the classification matrix,a new decision tree scheme is proposed,aiming to optimize computing speed.Finally,the program is compared with the ID3 algorithm through experiment.Experimental results show that the optimized scheme is obviously better than the original one in performance.