A Distributed C4.5 Decision Tree Algorithm Based on MapReduce
PAN Junhui
WANG Hui
ZHANG Qiang
WANG Haochang
Abstract：C4.5 decision tree is an effective algorithm for the extraction of classification rules.The algorithm has achieved good results in the processing of medium and small data sets,but its direct application to large data sets is limited by many aspects,while the distributed implementation of the algorithm by MapReduce framework is very convenient.Thus combining MapReduce with C4.5 decision tree,this paper proposes a distributed C4.5 decision tree algorithm based on MapReduce(MRCTA).The algo-rithm through preserving the merits of the C4.5 decision tree itself,in the first place in the structure of the decision tree node uses MapReduce to parallel computing of the splitting attribute,then it uses the optimal split attribute of data with the help of distributed segmentation to generate new nodes,at the same time in order to avoid over-learning,the tree depth and the number of samples coverd by nodes and the proportion of categories are taken as the conditions for the termination of the algorithm.Finally,the effec-tiveness and efficiency of the algorithm are compared and analyzed through experiments.
Keywords：decision treedistributed algorithmparallel computingMapReduce
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 327-331 )