Metric based attribute reduction in dynamic
decision tables

Abstract. In the past two decades, several results appeared on feature reduction applying rough set theory. However, most of these methods are implemented on static decision tables. Using a distance measure, in this paper we propose algorithms to find the reducts of decision tables when adding or deleting objects. Since we can avoid re-running the original algorithms over the entire set of objects, our methods significantly reduce the running time for attribute reduction in dynamic data.