Abstract
Classification and regression are fundamental data mining techniques. The goal of such techniques is to build predictors based on a training dataset and use them to predict the properties of new data. For a wide range of techniques, combining predictors built on samples from the training dataset provides lower error rates, faster construction, or both, than a predictor built from the entire training dataset. This provides a natural parallelization strategy in which predictors based on samples are built independently and hence concurrently. We discuss the performance implications for two subclasses: those in which predictors are independent, and those in which knowing a set of predictors reduces the difficulty of finding a new one.
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© 2002 Springer-Verlag Berlin Heidelberg
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Skillicorn, D.B. (2002). Parallel Predictor Generation. 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_9
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DOI: https://doi.org/10.1007/3-540-46502-2_9
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