基于Map-Reduce框架的C4.5分布式改进算法

Animproved C4.5 distributed algorithm based on map-reduce framework

为了利用决策树分类算法解决大规模数据集上的分类问题,提出了一种基于经典C4.5决策树算法的分布式算法,在Map-Reduce分布式计算系统框架下,利用分布式思想构造决策树节点,解决了数据量大和数据复杂度高导致分类时运行速度慢的问题。仿真结果表明,与经典C4.5算法相比,该分布式算法具有较高的分类准确率及较好的speedup、scaleup和sizeup性能。

To take advantage of the decision tree classification algorithm to solve classification problems in large-scale da-ta set,a distributed algorithm is proposed based on classical C4. 5 decision tree algorithm. In the Map-Reduce under theframework of distributed computing systems distributed idea is used to construct the decision tree node,and the data volumeand high data complexity leading to slow classification of running speed is solved. The simulation results show that comparedwith the classical C4. 5 algorithm,the feasibility of the distributed algorithm has higher and better speedup,scaleup and sizeupperformance.