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Exploiting feature relationships towards stable feature selection

Kamkar, Iman, Gupta, Sunil, Phung, Dinh and Venkatesh, Svetha 2015, Exploiting feature relationships towards stable feature selection, in DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 1-10, doi: 10.1109/DSAA.2015.7344859.

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Title Exploiting feature relationships towards stable feature selection
Author(s) Kamkar, Iman
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Data Science and Advanced Analytics. Conference (2015 : Paris, France)
Conference location Paris, France
Conference dates 19-21 Oct. 2015
Title of proceedings DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics
Editor(s) Gaussier, Eric
Cao, Longbing
Gallinari, Patrick
Kwok, James
Pasi, Gabriella
Zaiane, Osmar
Publication date 2015
Start page 1
End page 10
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) stability
Lasso
correlated features
prediction
Summary Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods.
ISBN 9781467382724
Language eng
DOI 10.1109/DSAA.2015.7344859
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081985

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