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Parallel MARS Algorithm Based on B-splines

Author

Listed:
  • Sergey Bakin

    (The Australian National University)

  • Markus Hegland

    (The Australian National University)

  • Michael R. Osborne

    (The Australian National University)

Abstract

Summary We investigate one of the possible ways for improving Friedman’s Multivariate Adaptive Regression Splines (MARS) algorithm designed for flexible modelling of high-dimensional data. In our version of MARS called BMARS we use B-splines instead of truncated power basis functions. The fact that B-splines have compact support allows us to introduce the notion of a “scale” of a basis function. The algorithm starts building up models by using large-scale basis functions and switches over to a smaller scale after the fitting ability of the large scale splines has been exhausted. The process is repeated until the prespecified number of basis functions has been produced. In addition, we discuss a parallelisation of BMARS as well as an application of the algorithm to processing of a large commercial data set. The results demonstrate the computational efficiency of our algorithm and its ability to generate models competitive with those of the original MARS.

Suggested Citation

  • Sergey Bakin & Markus Hegland & Michael R. Osborne, 2000. "Parallel MARS Algorithm Based on B-splines," Computational Statistics, Springer, vol. 15(4), pages 463-484, December.
    • Handle: RePEc:spr:compst:v:15:y:2000:i:4:d:10.1007_pl00022715
    DOI: 10.1007/PL00022715
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    Cited by:

    1. España, Victor J. & Aparicio, Juan & Barber, Xavier & Esteve, Miriam, 2024. "Estimating production functions through additive models based on regression splines," European Journal of Operational Research, Elsevier, vol. 312(2), pages 684-699.

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