Abstract
In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrated. We focus on learning a single model from a set of disjoint data sets, which are distributed across a set of computers. The model is a set of rules. The distributed data sets may be disjoint for any of several reasons. In our approach, the first step is to construct a rule set (model) for each of the original disjoint data sets. Then rule sets are merged until an eventual final rule set is obtained which models the aggregate data. We show that this approach compares to directly creating a rule set from the aggregate data and promises faster learning. Accuracy can drop off as the degree of parallelism increases. However, an approach has been developed to extend the degree of parallelism achieved before this problem takes over.
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Hall, L.O., Chawla, N., Bowyer, K.W., Kegelmeyer, W.P. (2000). Learning Rules from Distributed Data. In: Zaki, M.J., Ho, CT. (eds) Large-Scale Parallel Data Mining. Lecture Notes in Computer Science(), vol 1759. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46502-2_11
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DOI: https://doi.org/10.1007/3-540-46502-2_11
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