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arXiv · 1703.06990

Metalearning for Feature Selection

Ben Goertzel·Nil Geisweiller·Chris Poulin
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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.

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Ben Goertzel, Nil Geisweiller, Chris Poulin. 2017-03-20. Metalearning for Feature Selection. https://arxiv.org/abs/1703.06990

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