Outliers Detection in Multi-label Datasets
Bello,Marilyn Nápoles,Gonzalo Morera,Rafael Vanhoof,Koen Bello,Rafael
Bello,Marilyn
Nápoles,Gonzalo
Morera,Rafael
Vanhoof,Koen
Bello,Rafael
Abstract
In many knowledge discovery applications, finding outliers, i.e. objects that behave in an unexpected way or have abnormal properties, is more interesting than finding inliers in a dataset. Outlier detection is important for many applications, including those related to intrusion detection, credit card fraud, and criminal activity in e-commerce. Several methods of outlier detection have been proposed, and even many of them from the perspective of Rough Set Theory, but at the moment none of them is specifically intended for multi-label datasets. In this paper, we propose a method that measures the degree of anomaly of an object in a multi-label dataset. This score or measure quantifies the degree of irregularity of an object with respect to the dataset. In addition, a method for generating anomalies in this type of datasets is proposed. From these synthetic datasets, the efficacy of the proposed method is proved. The results show the superiority of our proposal over other methods in the literature adapted to multi-label problems.
Description
Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Date
2020
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Publisher
Springer Science and Business Media Deutschland GmbH
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Keywords
Knowledge discovery, Multi-label datasets, Outlier detection, Outlier generation, Rough set theory
Citation
Bello, M, Nápoles, G, Morera, R, Vanhoof, K & Bello, R 2020, Outliers Detection in Multi-label Datasets. in L Martínez-Villaseñor, H Ponce, O Herrera-Alcántara & F A Castro-Espinoza (eds), Advances in Soft Computing - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12468 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 65-75, 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, 12/10/20. https://doi.org/10.1007/978-3-030-60884-2_5
License
info:eu-repo/semantics/closedAccess