A parallel tree node splitting criterion for fuzzy decision trees

摘要

Fuzzy decision trees are one of the most important extensions of decision trees for symbolicknowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy informationgain as ameasure to construct the tree node splitting criteria. These criteria play a criticalrole in the construction of decision trees. However, many of the criteria can only work well onsmall-scale ormedium-scale data sets, and cannot directly deal with large-scale data sets on theaccount of some limiting factors such as memory capacity, execution time, and data complexity.Parallel computing is one way to overcome these problems; in particular, MapReduce is onemainstream solution of parallel computing. In this paper, we design a parallel tree node splittingcriterion (MR-NSC) based on fuzzy information gain via MapReduce, which is completedequivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalencybetween the proposed MR-NSC algorithm and the traditional unparallel way through 22UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on twolarge-scale data sets.