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Greedy feature construction

Oglic, Dino; Gaertner, Thomas

Authors

Dino Oglic dino.oglic@nottingham.ac.uk

Thomas Gaertner thomas.gaertner@nottingham.ac.uk



Abstract

We present an effective method for supervised feature construction. The main goal of the approach is to construct a feature representation for which a set of linear hypotheses is of sufficient capacity -- large enough to contain a satisfactory solution to the considered problem and small enough to allow good generalization from a small number of training examples. We achieve this goal with a greedy procedure that constructs features by empirically fitting squared error residuals. The proposed constructive procedure is consistent and can output a rich set of features. The effectiveness of the approach is evaluated empirically by fitting a linear ridge regression model in the constructed feature space and our empirical results indicate a superior performance of our approach over competing methods.

Citation

Oglic, D., & Gaertner, T. (2016). Greedy feature construction. Advances in Neural Information Processing Systems, 29,

Journal Article Type Conference Paper
Conference Name 30th Conference on Neural Information Processing Systems (NIPS 2016)
End Date Dec 10, 2016
Acceptance Date Aug 12, 2016
Publication Date Dec 5, 2016
Deposit Date Nov 9, 2016
Publicly Available Date Dec 5, 2016
Journal Advances in Neural Information Processing Systems
Electronic ISSN 1049-5258
Publisher Massachusetts Institute of Technology Press
Peer Reviewed Peer Reviewed
Volume 29
Public URL http://eprints.nottingham.ac.uk/id/eprint/38608
Publisher URL http://papers.nips.cc/paper/6557-greedy-feature-construction
Related Public URLs http://papers.nips.cc/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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