Metalearning for Feature Selection
Abstract
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.
Cite
@article{arxiv.1703.06990,
title = {Metalearning for Feature Selection},
author = {Ben Goertzel and Nil Geisweiller and Chris Poulin},
journal= {arXiv preprint arXiv:1703.06990},
year = {2017}
}