数据挖掘中改进的C4.5决策树分类算法

针对传统C4.5决策树分类算法需要进行多次扫描,导致运行效率低的缺陷,提出一种新的改进C4.5决策树分类算法.通过优化信息增益推导算法中相关的对数运算,以减少决策树分类算法的运行时间;将传统算法中连续属性的简单分裂属性改进为最优划分点分裂处理,以提高算法效率.实验结果表明,改进的C4.5决策树分类算法相比传统的C4.5决策树分类算法极大提高了执行效率,减小了需求空间.

Improved C45 Decision Tree Classification Algorithm in Data Mining

Aiming at the problem that the algorithm for traditional C45 decision tree classification algorithm needed to be scanned several times, resulting in defects of running low efficiency, the author proposed a new improvedC45 decision tree classification algorithm by optimizing the logarithmic operation related information gain derivation algorithm in order to reduce the running time of the decision tree classification algorithm. And the simple split attribute of the continuous attributes in the traditional algorithm was improved to the optimal partitionpoint splitting processing in order to improve the efficiency of the algorithm. Experimental results show that compared with the traditional C45decision tree classification algorithm, the improved C45 decision tree classification algorithm greatly improves the execution efficiency and reduces the demand space.