title: A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data.
abstract: In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.
authors.forename: Dino
authors.initials: D
authors.lastname: Ienco
authors.forename: Ruggero G
authors.initials: RG
authors.lastname: Pensa
authors.forename: Rosa
authors.initials: R
authors.lastname: Meo