Statistics and Its Interface

Volume 2 (2009)

Number 3

Boosting on the functional ANOVA decomposition

Pages: 361 – 368

DOI: http://dx.doi.org/10.4310/SII.2009.v2.n3.a9

Authors

Jinseog Kim (Department of Statistics, Dongguk University, Korea)

Yongdai Kim (Department of Statistics, Seoul National University, Korea)

Yuwon Kim (NHN Corp., Korea)

Sunghoon Kwon (Department of Statistics, Seoul National University, Korea)

Sangin Lee (Department of Statistics, Seoul National University, Korea)

Abstract

A boosting algorithm on the functional ANOVA decomposition, called ANOVA boosting, is proposed. The main idea of ANOVA boosting is to estimate each component in the functional ANOVA decomposition by combining many base (weak) learners. A regularization procedure based on the L1 penalty is proposed to give a componentwise sparse solution and an efficient computing algorithm is developed. Simulated as well as bench mark data sets are analyzed to compare ANOVA boosting and standard boosting. ANOVA boosting improves prediction accuracy as well as interpretability by estimating the components directly and providing componentwisely sparser models.

Keywords

functional ANOVA decomposition, boosting, variable selection

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