Find in Library

To improve the computational efficiency and classification accuracy in the context of big data, an optimized parallel random forest algorithm is proposed based on the Spark computing framework. First, a new Gini coefficient is defined to reduce the impact of feature redundancy for higher classification accuracy. Next, to reduce the number of candidate split points and Gini coefficient calculations for continuous features, an approximate equal-frequency binning method is proposed to determine the optimal split points efficiently. Finally, based on Apache Spark computing framework, the forest sampling index (FSI) table is defined to speed up the parallel training process of decision trees and reduce data communication overhead. Experimental results show that the proposed algorithm improves the efficiency of constructing random forests while ensuring classification accuracy, and is superior to Spark-MLRF in terms of performance and scalability.