ML Anthology

Generalized Skewing for Functions with Continuous and Nominal Attributes

Soumya Ray, David Page
ICML 2005 pp. 705-712
doi:10.1145/1102351.1102440 /icml/2005/ray2005icml-generalized/

Abstract

This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary variables. In this paper, we extend skewing to directly handle functions of continuous and nominal variables. We present experiments with randomly generated functions and a number of real world datasets to evaluate the algorithm's accuracy. Our results indicate that our algorithm almost always outperforms an Information Gain-based decision tree learner.

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Cite

Text

Ray and Page. "Generalized Skewing for Functions with Continuous and Nominal Attributes." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102440

Markdown

[Ray and Page. "Generalized Skewing for Functions with Continuous and Nominal Attributes." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/ray2005icml-generalized/) doi:10.1145/1102351.1102440

BibTeX

@inproceedings{ray2005icml-generalized,
  title     = {{Generalized Skewing for Functions with Continuous and Nominal Attributes}},
  author    = {Ray, Soumya and Page, David},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {705-712},
  doi       = {10.1145/1102351.1102440},
  url       = {https://mlanthology.org/icml/2005/ray2005icml-generalized/}
}