Metric based attribute reduction in decision tables
Long Giang Nguyen · 2012
Abstract—In an information system, each subset of attributes determines knowledge structure on the set of objects, in which each element is an equivalence class. Thus, a metric which is defined on knowledge structures is established on the attribute sets. Once a metric is established, we can use the metric to measure attributes distance, cluster and discover important attributes. As a result, effective algorithms are constructed to solve attribute reduction in information systems. With metric on knowledge structures based on the Jaccard distance between two finite sets, this paper proposes a new method for attribute reduction in decision table. The paper proves theoretically and experimentally that this metric method is more effective than other methods based on conditional Shannon entropy. I.