Instance Ranking in Noisy Databases

 

Xindong Wu

 

Abstract

Given a noisy database, how to locate erroneous instances and attributes and rank suspicious instances based on their impacts with the system performance is an interesting and important research issue. This talk presents an Error Detection and Impact-sensitive instance Ranking (EDIR) mechanism to address this problem, and also discusses the application of this mechanism to deception detection.