Interpretable decision-tree induction in a big data parallel framework - Weinberg, Abraham Itzhak; Last, Mark (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: big data; parallel computing; MapReduce; decision trees; editing distance; tree similarity