A Parallel Solver for Generalised Additive Models
Date
1999
Authors
Hegland, Markus
McIntosh, I
Turlach, B
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Publisher
Elsevier
Abstract
An implementation of the backfitting algorithm for generalised additive models which is suitable for parallel computing is described. This implementation is designed to handle large data sets such as those occurring in data mining with several millions of observations on several hundreds of variables. For such large data sets it is crucial to have a fast, parallel implementation for fitting generalised additive models to allow an exploratory analysis of the data within a reasonable time. The approach used divides the data into several blocks (groups) and fits a (generalised) additive model to each block. These models are then merged to a single, final model. It is shown that this approach is very efficient as it allows the algorithm to adapt to the structure of the parallel computer (number of processors and amount of internal memory).
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Keywords: Computer systems programming; Data mining; Data reduction; Data structures; Parallel algorithms; Backfitting algorithms; Generalized additive models; Parallel processing systems Additive models; Backfitting; Data mining; Local scoring; Parallel algorithms
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Source
Computational Statistics and Data Analysis
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Journal article
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2037-12-31
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