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Recursive Feature Elimination by Sensitivity Testing.


ABSTRACT: There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With membership queries, one can check whether changing the value of a feature in an example changes the label. In the real-world, we usually cannot get answers to such queries, so our approach instead makes these queries to a trained (imperfect) non-linear model. Because SVMs are widely used in bioinformatics, our empirical results use a real-world cancer genomics problem; because ground truth is not known for this task, we discuss the potential insights provided. We also evaluate on synthetic data where ground truth is known.

SUBMITTER: Escanilla NS 

PROVIDER: S-EPMC6887481 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Recursive Feature Elimination by Sensitivity Testing.

Escanilla Nicholas Sean NS   Hellerstein Lisa L   Kleiman Ross R   Kuang Zhaobin Z   Shull James D JD   Page David D  

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications 20181201


There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With memb  ...[more]

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