Metalearning for Feature Selection
This work addresses feature selection for optimization problems like classification, offering potential efficiency gains, but it appears incremental as it builds on existing metalearning concepts.
The paper tackles the problem of feature selection in optimization problems by introducing a novel characterization of feature quality and integrating it into metalearning, demonstrating that feature metalearning can provide significant speedup over standard heuristics in supervised text classification.
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.