Feature Relevance Bounds for Linear Classification

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Abstract

Biomedical applications often aim for an identification of relevant features for a given classification task, since these carry the promise of semantic insight into the underlying process.
For correlated input dimensions, feature relevances are not unique, and the identification of meaningful subtle biomarkers remains a challenge.
One approach is to identify intervals for the possible relevance of given features, a problem related to all relevant feature determination.
In this contribution, we address the important case of linear classifiers and we transfer the problem how to infer feature relevance bounds to a convex optimization problem.
We demonstrate the superiority of the resulting technique in comparison to popular feature-relevance determination methods in several benchmarks.

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