Title: Contextual Outlier Interpretation,
Abstract: Outlier detection plays an essential role in many data-driven applications to
identify isolated instances that are different from the majority. While many
statistical learning and data mining techniques have been used for developing
more effective outlier detection algorithms, the interpretation of detected
outliers does not receive much attention. Interpretation is becoming
increasingly important to help people trust and evaluate the developed models
through providing intrinsic reasons why the certain outliers are chosen. It is
difficult, if not impossible, to simply apply feature selection for explaining
outliers due to the distinct characteristics of various detection models,
complicated structures of data in certain applications, and imbalanced
distribution of outliers and normal instances. In addition, the role of
contrastive contexts where outliers locate, as well as the relation between
outliers and contexts, are usually overlooked in interpretation. To tackle the
issues above, in this paper, we propose a novel Contextual Outlier
INterpretation (COIN) method to explain the abnormality of existing outliers
spotted by detectors. The interpretability for an outlier is achieved from
three aspects: outlierness score, attributes that contribute to the
abnormality, and contextual description of its neighborhoods. Experimental
results on various types of datasets demonstrate the flexibility and
effectiveness of the proposed framework compared with existing interpretation
approaches.