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Paper details
Number 4 - December 2017
Volume 27 - 2017
Interpretable decision-tree induction in a big data parallel framework
Abraham Itzhak Weinberg, Mark Last
Abstract
When running data-mining algorithms on big data platforms, a parallel, distributed framework, such as MAPREDUCE, may
be used. However, in a parallel framework, each individual model fits the data allocated to its own computing node without
necessarily fitting the entire dataset. In order to induce a single consistent model, ensemble algorithms such as majority
voting, aggregate the local models, rather than analyzing the entire dataset directly. Our goal is to develop an efficient
algorithm for choosing one representative model from multiple, locally induced decision-tree models. The proposed SySM
(syntactic similarity method) algorithm computes the similarity between the models produced by parallel nodes and chooses
the model which is most similar to others as the best representative of the entire dataset. In 18.75% of 48 experiments on
four big datasets, SySM accuracy is significantly higher than that of the ensemble; in about 43.75% of the experiments,
SySM accuracy is significantly lower; in one case, the results are identical; and in the remaining 35.41% of cases the
difference is not statistically significant. Compared with ensemble methods, the representative tree models selected by the
proposed methodology are more compact and interpretable, their induction consumes less memory, and, as confirmed by
the empirical results, they allow faster classification of new records.
Keywords
big data, parallel computing, MAPREDUCE, decision trees, editing distance, tree similarity