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Canonical Forest

Author

Listed:
  • Yu-Chuan Chen
  • Hyejung Ha
  • Hyunjoong Kim
  • Hongshik Ahn

Abstract

We propose a new classification ensemble method named Canonical Forest. The new method uses canonical linear discriminant analysis (CLDA) and bootstrapping to obtain accurate and diverse classifiers that constitute an ensemble. We note CLDA serves as a linear transformation tool rather than a dimension reduction tool. Since CLDA will find the transformed space that separates the classes farther in distribution, classifiers built on this space will be more accurate than those on the original space. To further facilitate the diversity of the classifiers in an ensemble, CLDA is applied only on a partial feature space for each bootstrapped data. To compare the performance of Canonical Forest and other widely used ensemble methods, we tested them on 29 real or artificial data sets. Canonical Forest performed significantly better in accuracy than other ensemble methods in most data sets. According to the investigation on the bias and variance decomposition, the success of Canonical Forest can be attributed to the variance reduction. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Yu-Chuan Chen & Hyejung Ha & Hyunjoong Kim & Hongshik Ahn, 2014. "Canonical Forest," Computational Statistics, Springer, vol. 29(3), pages 849-867, June.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:3:p:849-867
    DOI: 10.1007/s00180-013-0466-x
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    References listed on IDEAS

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    1. Hans Kestler & Ludwig Lausser & Wolfgang Lindner & Günther Palm, 2011. "On the fusion of threshold classifiers for categorization and dimensionality reduction," Computational Statistics, Springer, vol. 26(2), pages 321-340, June.
    2. Ahn, Hongshik & Moon, Hojin & Fazzari, Melissa J. & Lim, Noha & Chen, James J. & Kodell, Ralph L., 2007. "Classification by ensembles from random partitions of high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6166-6179, August.
    3. Kenichi Hayashi, 2012. "A boosting method with asymmetric mislabeling probabilities which depend on covariates," Computational Statistics, Springer, vol. 27(2), pages 203-218, June.
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