A hybrid approach to outlier detection based on boundary region

In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers – objects who behave in an unexpected way or have abnormal properties. The identification of outliers is important for many applications such as intrusion detection, credit card fraud, criminal activities in electronic commerce, medical diagnosis and anti-terrorism, etc. In this paper, we propose a hybrid approach to outlier detection, which combines the opinions from boundary-based and distance-based methods for outlier detection (Jiang et al., 2005, Jiang et al., 2009 and Knorr and Ng, 1998). We give a novel definition of outliers – BD (boundary and distance)-based outliers, by virtue of the notion of boundary region in rough set theory and the definitions of distance-based outliers. An algorithm to find such outliers is also given. And the effectiveness of our method for outlier detection is demonstrated on two publicly available databases.

► We propose a hybrid approach to outlier detection. ► Our method combines the opinions from boundary-based and distance-based methods. ► Our method adopts different attitudes to objects from different parts of the data set. ► We define a hybrid outlier factor to indicate the degree of outlierness of an object. ► The effectiveness of our method is demonstrated on two publicly available data sets.