2013 FeaFinerBiomarkerIdentification

• (Zhou et al., 2013) ⇒ Jiayu Zhou, Zhaosong Lu, Jimeng Sun, Lei Yuan, Fei Wang, and Jieping Ye. (2013). “FeaFiner: Biomarker Identification from Medical Data through Feature Generalization and Selection.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487671

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+FeaFiner%3A+Biomarker+Identification+from+Medical+Data+through+Feature+Generalization+and+Selection
• http://dl.acm.org/citation.cfm?id=2487575.2487671&preflayout=flat#citedby

Quotes

Author Keywords

• Augmented lagrangian; biomarkers; data mining; feature generalization; feature selection; sparse learning; spectral gradient descent

Abstract

Traditionally, feature construction and feature selection are two important but separate processes in data mining. However, many real world applications require an integrated approach for creating, refining and selecting features. To address this problem, we propose FeaFiner (short for Feature Refiner), an efficient formulation that simultaneously generalizes low-level features into higher level concepts and then selects relevant concepts based on the target variable. Specifically, we formulate a double sparsity optimization problem that identifies groups in the low-level features, generalizes higher level features using the groups and performs feature selection. Since in many clinical researches non-overlapping groups are preferred for better interpretability, we further improve the formulation to generalize features using mutually exclusive feature groups. The proposed formulation is challenging to solve due to the orthogonality constraints, non-convexity objective and non-smoothness penalties. We apply a recently developed augmented Lagrangian method to solve this formulation in which each subproblem is solved by a non-monotone spectral projected gradient method. Our numerical experiments show that this approach is computationally efficient and also capable of producing solutions of high quality. We also present a generalization bound showing the consistency and the asymptotic behavior of the learning process of our proposed formulation.

Finally, the proposed FeaFiner method is validated on Alzheimer's Disease Neuroimaging Initiative dataset, where low-level biomarkers are automatically generalized into robust higher level concepts which are then selected for predicting the disease status measured by Mini Mental State Examination and Alzheimer's Disease Assessment Scale cognitive subscore. Compared to existing predictive modeling methods, FeaFiner provides intuitive and robust feature concepts and competitive predictive accuracy.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 FeaFinerBiomarkerIdentification	Lei Yuan Jieping Ye Jimeng Sun Fei Wang Jiayu Zhou Zhaosong Lu			FeaFiner: Biomarker Identification from Medical Data through Feature Generalization and Selection				10.1145/2487575.2487671		2013