关键词: 随机森林, Spark, 主成分分析（PCA）, 分层抽样, 误差约束, 数据划分, 数据复用

Abstract: In the context of big data, the random forest algorithm has large covariance matrix, insufficient coverage of subspace feature information and high node communication overhead. A parallel random forest algorithm based on PCA and subspace hierarchical selection, PLA-PRF（PCA and subspace layer sampling on parallel random forest algorithm）. For the initial feature set, a PCA-based matrix factorization strategy（MFS） is proposed to extract principal component features to solve the problem of large covariance matrix in the process of feature transformation. Based on the obtained principal component features, a hierarchical subspace construction algorithm（error-constrained hierarchical subspace construction algorithm, EHSCA） based on error constraints is proposed, which selects pheromone features hierarchically, constructs feature subspaces, and solves the problem of insufficient coverage of subspace feature information. In the process of parallel training decision trees in the Spark environment, a data reuse strategy（DRS） is designed to solve the problem of high node communication overhead. By vertically dividing RDD data objects, it improves the performance of the distributed environment. Data utilization rate solves the problem of high node communication overhead. Experimental results show that PLA-PRF has better classification effect and higher parallelization efficiency.

Key words: random forest, Spark, princepal component analysis（PCA）, layer sampling, error constraint, data partition, data reuse