Query-sensitive Feature Selection for Lazy Learners

Feature selection contributes to increasing many learners´ accuracy by identifying and removing irrelevant features in multidimensional datasets. Conventional feature selection methods determine the optimal feature subset independently from and prior to the introduction of a new query. In general, some features will be relevant only in certain tasks. We argue that a query, as an indicator of the attention focus and current task, is a major part of the context and should be involved in the determination of the final feature subset. In this paper we attempt to propose a query-sensitive feature selection model, present two algorithms for applying such a feature selection method, and test their effectiveness by comparing their performances to those of the conventional selection algorithms. Our experiments are executed under a nearest neighbor classification environment and the results show a consistent improvement in the classification performance when a query-sensitive feature subset is selected and used for measuring similarities between the query and other instances. The results suggest that the performance of a lazy learner has the potential to increase through query-sensitive feature selection