Supervised Feature Selection via Dependence Estimation

Song, L; Smola, AJ; Gretton, A; Borgwardt, KM; Bedo, J

doi:10.1145/1273496.1273600

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会議論文

Supervised Feature Selection via Dependence Estimation

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Gretton, A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1273496.1273600
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ICML-2007-Song-Gretton.pdf
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引用

Song, L., Smola, A., Gretton, A., Borgwardt, K., & Bedo, J. (2007). Supervised Feature Selection via Dependence Estimation. In Z., Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 823-830). New York, NY, USA: ACM Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-CD6B-B

要旨

We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.