2010 FeatureSelectionforSupportVecto

• (Yang et al., 2010) ⇒ Jian-Bo Yang, and Chong-Jin Ong. (2010). “Feature Selection for Support Vector Regression Using Probabilistic Prediction.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835849

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Cited By

• http://scholar.google.com/scholar?q=%22Feature+selection+for+support+vector+regression+using+probabilistic+prediction%22+2010
• http://portal.acm.org/citation.cfm?id=1835849&preflayout=flat#citedby

Quotes

Abstract

This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiment shows that the proposed method generally performs better, and at least as well as the existing methods, with notable advantage when the data set is sparse.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 FeatureSelectionforSupportVecto	Jian-Bo Yang Chong-Jin Ong			Feature Selection for Support Vector Regression Using Probabilistic Prediction		KDD-2010 Proceedings		10.1145/1835804.1835849		2010