Parallel Predictor Generation

Authors:
David B. Skillicorn
Affiliations:
-
Venue:
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Year:
1999

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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.