Online group feature selection

Authors:
Jing Wang;Zhong-Qiu Zhao;Xuegang Hu;Yiu-Ming Cheung;Meng Wang;Xindong Wu
Affiliations:
College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China and Department of Computer Science, Hong Kong Baptist University, Hong Kong, China;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China;Department of Computer Science, Hong Kong Baptist University, Hong Kong, China;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China;College of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China and Department of Computer Science, University of Vermont, Burlington
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Online feature selection with dynamic features has become an active research area in recent years. However, in some real-world applications such as image analysis and email spam filtering, features may arrive by groups. Existing online feature selection methods evaluate features individually, while existing group feature selection methods cannot handle online processing. Motivated by this, we formulate the online group feature selection problem, and propose a novel selection approach for this problem. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we use spectral analysis to select discriminative features in each group when it arrives. In the inter-group selection, we use Lasso to select a globally optimal subset of features. This 2-stage procedure continues until there are no more features to come or some predefined stopping conditions are met. Extensive experiments conducted on benchmark and real-world data sets demonstrate that our proposed approach outperforms other state-of-the-art online feature selection methods.