Fast and robust selection of highly-correlated features in regression problems

Maier A, Rodriguez Salas D (2017)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2017

Publisher: Institute of Electrical and Electronics Engineers Inc.

Edited Volumes: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Pages Range: 1-4

Conference Proceedings Title: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)

Event location: Nagoya University, Nagoya, Japan JP

ISBN: 9784901122160

URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Dalia17-IARP.pdf

DOI: 10.23919/MVA.2017.7986905

Abstract

Feature selection for regression problems can be highly beneficial in terms of robustness and execution speed. The Correlation-based Feature Selection (CFS) algorithm, in the attempt to find the best feature subset, evaluates different subsets and selects the one with the highest “goodness”. Such goodness is based on the co-relation between the addition of all features in the subset with the output variable. However, such a simple addition assumes that all features have the same weight in the output variable. This, in turn, assumes that all features are uncorrelated with each other. By considering an optimal weighting instead, a more robust measurement of the goodness can be obtained; however, such weighting is computationally expensive. In this paper, a feature selection algorithm named “R-fast” which considers the β coefficients in the standardized linear regression model as optimal weights is proposed. We also present a technique to quickly estimate approximations to the β coefficients. Our algorithm was evaluated using Multiple Regression Analysis (MRA) over 8 synthetic and 10 real-world datasets. Results show that, when selecting features with our proposed algorithm, MRA's performance is better than or equal to those obtained when selecting features with others well-known filter algorithms and when no selection is performed.

Authors with CRIS profile

Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Dalia Rodriguez Salas Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Maier, A., & Rodriguez Salas, D. (2017). Fast and robust selection of highly-correlated features in regression problems. In 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) (pp. 1-4). Nagoya University, Nagoya, Japan, JP: Institute of Electrical and Electronics Engineers Inc..

MLA:

Maier, Andreas, and Dalia Rodriguez Salas. "Fast and robust selection of highly-correlated features in regression problems." Proceedings of the International Conference on Machine Vision Applications, Nagoya University, Nagoya, Japan Institute of Electrical and Electronics Engineers Inc., 2017. 1-4.

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