Sciweavers

Share
ICPR
2010
IEEE

The Problem of Fragile Feature Subset Preference in Feature Selection Methods and a Proposal of Algorithmic Workaround

10 years 1 months ago
The Problem of Fragile Feature Subset Preference in Feature Selection Methods and a Proposal of Algorithmic Workaround
Abstract—We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selection criterion value always indicates more preferable subset even if the value difference is marginal. This assumption ignores the reliability issues of particular feature preferences, overfitting and feature acquisition cost. We propose an algorithmic extension applicable to many standard feature selection methods allowing better control over feature subset preference. We show experimentally that the proposed mechanism is capable of reducing the size of selected subsets as well as improving classifier generalization. Keywords-feature selection, machine learning, over-fitting, classification, feature weights, weighted features, feature acquisition cost
Petr Somol, Jiri Grim, Pavel Pudil
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2010
Where ICPR
Authors Petr Somol, Jiri Grim, Pavel Pudil
Comments (0)
books