Can I read Metalearning for Feature Selection on EtoBox?

Metalearning for Feature Selection by Goertzel, Ben; Geisweiller, Nil; Poulin, Chris is a scholarly article available to read on EtoBox.

What is Metalearning for Feature Selection about?

A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is provided; and a proposal regarding the integration of quality based feature selection into metalearning is suggested, wherein the quality of a feature for a problem is estimated using knowledge about related features in the context of related problems. Results are presented regarding extensive testing of this "feature metalearning" approach on supervised text classification problems; it is demonstrated that, in this context, feature metalearning can provide significant and sometimes dramatic speedup over standard feature selection heuristics.

Author

Goertzel, Ben; Geisweiller, Nil; Poulin, Chris

Published

2017

Language

EN