Distributed Fuzzy Decision Trees For Big Data

Computer Science

Fuzzy Decision Trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the Map Reduce programming model for generating both binary and multi-way FDTs from big data. The scheme relies on a novel distributed fuzzy discretise that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are therefore used as input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behaviour of the scheme along three dimensions. Performance in terms of classification accuracy, model complexity and execution time, Scalability varying the number of computing units. Ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with modest commodity hardware support.

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