一种基于变精度粗糙集的C4.5决策树改进算法

针对C4.5决策树构造复杂、分类精度不高等问题,提出了一种基于变精度粗糙集的决策树构造改进算法.该算法采用近似分类质量作为节点选择属性的启发函数,与信息增益率相比,该标准更能准确地刻画属性分类的综合贡献能力,同时对噪声有一定的抑制能力.此外还针对两个或两个以上属性的近似分类质量相等的特殊情形,给出了如何选择最优的分类属...

Improved C4.5 decision trees algorithm based on variable precision rough set

Aiming at the problems of complexisity and relatively low classification accuracy of decision trees constructed by C4.5 algorithm, this paper proposed a new decision trees classification algorithm (VPRSC4.5) based on the variable precision rough set (VPRS), which took the approximate quality of classification as the heuristic function in order to alleviate the effect of noise data on choosing splitting attributes. It also gave out the solution to the problem how to choose the best attributes as the node when two or more attributes had the same value of approximate quality of classification. Experiments prove that the size and classification accuracy of the decision trees generated by the improved algorithm is superior to the C4.5 algorithm.