caa a22 4500 605478376 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5507-y doi (NATIONALLICENCE)springer-10.1007/s10994-015-5507-y A decomposition of the outlier detection problem into a set of supervised learning problems [Elektronische Daten] [Heiko Paulheim, Robert Meusel] Outlier detection methods automatically identify instances that deviate from the majority of the data. In this paper, we propose a novel approach for unsupervised outlier detection, which re-formulates the outlier detection problem in numerical data as a set of supervised regression learning problems. For each attribute, we learn a predictive model which predicts the values of that attribute from the values of all other attributes, and compute the deviations between the predictions and the actual values. From those deviations, we derive both a weight for each attribute, and a final outlier score using those weights. The weights help separating the relevant attributes from the irrelevant ones, and thus make the approach well suitable for discovering outliers otherwise masked in high-dimensional data. An empirical evaluation shows that our approach outperforms existing algorithms, and is particularly robust in datasets with many irrelevant attributes. Furthermore, we show that if a symbolic machine learning method is used to solve the individual learning problems, the approach is also capable of generating concise explanations for the detected outliers. The Author(s), 2015 Outlier detection nationallicence Machine learning nationallicence Outlier explanations nationallicence Paulheim Heiko Data and Web Science Group, University of Mannheim, Mannheim, Germany aut Meusel Robert Data and Web Science Group, University of Mannheim, Mannheim, Germany aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 509-531 0885-6125 100:2-3<509 2015 100 10994 https://doi.org/10.1007/s10994-015-5507-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5507-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Paulheim Heiko Data and Web Science Group, University of Mannheim, Mannheim, Germany aut NATIONALLICENCE 700 1- Meusel Robert Data and Web Science Group, University of Mannheim, Mannheim, Germany aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 509-531 0885-6125 100:2-3<509 2015 100 10994