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A parallel tree node splitting criterion for fuzzy decision trees
Author(s) -
Mu Yashuang,
Liu Xiaodong,
Wang Lidong,
Asghar Aamer Bilal
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5268
Subject(s) - node (physics) , computer science , fuzzy logic , decision tree , data mining , benchmark (surveying) , construct (python library) , theoretical computer science , mathematics , artificial intelligence , structural engineering , geodesy , engineering , programming language , geography
Summary Fuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small‐scale or medium‐scale data sets, and cannot directly deal with large‐scale data sets on the account 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 one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR‐NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR‐NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large‐scale data sets.