SOR: Scalable orthogonal regression for non-redundant feature selection and its healthcare applications

Dijun Luo, Fei Wang, Jimeng Sun, Marianthi Markatou, Jianying Hu, Shahram Ebadollahi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As more clinical information with increasing diversity become available for analysis, a large number of features can be constructed and leveraged for predictive modeling. Feature selection is a classic analytic component that faces new challenges due to the new applications: How to handle a diverse set of high dimensional features? How to select features with high predictive power, but low redundant information? How to design methods that can select globally optimal features with theoretical guarantee? How to incorporate and extend existing knowledge driven approach? In this paper, we present Scalable Orthogonal Regression (SOR), an optimization-based feature selection method with the following novelties: 1) Scalability: SOR achieves nearly linear scale-up with respect to the number of input features and the number of samples; 2) Optimality: SOR is formulated as an alternative convex optimization problem with theoretical convergence and global optimality guarantee; 3) Low-redundancy: Thanks to the orthogonality objective, SOR is designed specifically to select less redundant features without sacrificing quality; 4) Extendability: SOR can enhance an existing set of preselected features by adding additional features that complement the existing feature set but still with strong predictive power. We present evaluation results showing that SOR consistently outperforms state of the art feature selection methods in a range of quality metrics on several real world data sets. We demonstrate a case study of a large-scale clinical application for predicting early onset of Heart Failure (HF) using real Electronic Health Records (EHRs) data of over 10K patients for over 7 years. Leveraging SOR, we are able to construct accurate and robust predictive models and derive potential clinical insights.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Pages576-587
Number of pages12
ISBN (Print)9781611972320
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Other

Other12th SIAM International Conference on Data Mining, SDM 2012
Country/TerritoryUnited States
CityAnaheim, CA
Period4/26/124/28/12

ASJC Scopus subject areas

  • Computer Science Applications

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