Abstract: With the coming of the big data age,data information is increasing exponentially at a dramatic rate.The traditional classification algorithm will encounter great challenges.In order to improve the efficiency of classification algorithm,this paper proposd a parallel random forest algorithm based on discretization and the selection of the weak-correlation feature subspaces.This algorithm discretizes continuous attributes in data pretreatment phase.At the step of the selection of feature subspaces for growing decision trees,we used vector space modal of attributes to calculate the correlation between attributes,and then constructed the weak-correlation feature subspaces.This algorithm not only reduces the correlation among decision trees,but also improves the classifying effect of the random forest.We also designed and realized a double parallel method for building random forest model based on the MapReduce framework.This strategy goes a step further with its own charity efforts.

Key words: Random forest,Discretization,Weak-correlation feature subspaces,Parallel classification