Recursive Feature Elimination by Sensitivity Testing.
Proc Int Conf Mach Learn Appl
; 2018: 40-47, 2018 Dec.
Article
in En
| MEDLINE
| ID: mdl-31799516
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
Language:
En
Journal:
Proc Int Conf Mach Learn Appl
Year:
2018
Type:
Article