  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.
