Greedy feature construction

Oglic, Dino and Gaertner, Thomas (2016) Greedy feature construction. Advances in Neural Information Processing Systems, 29 . pp. 3945-3953. ISSN 1049-5258

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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.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Oglic, Dino
Date Deposited: 09 Nov 2016 13:32
Last Modified: 04 May 2020 18:27

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