Metalearning for Feature Selection.

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 for such an optimization problem is provided; and a proposal regarding the integration of quality based selection into is suggested, wherein the quality of a 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, can provide significant and sometimes dramatic speedup over standard selection heuristics.