A mixed learning strategy for finding typical testors in large datasets

This paper presents a mixed, global and local, learning strategy for finding typical testors in large datasets. The goal of the proposed strategy is to allow any search algorithm to achieve the most significant reduction possible in the search space of a typical testor-finding problem. The strategy is based on a trivial classifier which partitions the search space into four distinct classes and allows the assessment of each feature subset within it. Each class is handled by slightly different learning actions, and induces a different reduction in the search-space of a problem. Any typical testor-finding algorithm, whether deterministic or metaheuristc, can be adapted to incorporate the proposed strategy and can take advantage of the learned information in diverse manners.