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http://hdl.handle.net/1942/32465
Title: | Outliers Detection in Multi-label Datasets | Authors: | BELLO GARCIA, Marilyn NAPOLES RUIZ, Gonzalo Morera, Rafael VANHOOF, Koen Bello, Rafael |
Issue Date: | 2020 | Publisher: | Springer | Source: | Lecture notes in computer science, 12468 , p. 65 -75 | Series/Report: | Lecture Notes in Artificial Intelligence | Series/Report no.: | 12468 | 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. | Keywords: | Outlier detection;Outlier generation;Multi-label datasets;Rough set theory;Knowledge discovery | Document URI: | http://hdl.handle.net/1942/32465 | ISBN: | 978-3-030-60883-5 9783030608842 |
ISSN: | 0302-9743 | DOI: | 10.1007/978-3-030-60884-2_5 | ISI #: | 000771851800005 | Rights: | Springer Nature Switzerland AG 2020 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2023 vabb 2022 |
Appears in Collections: | Research publications |
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32465a.pdf | Peer-reviewed author version | 702.92 kB | Adobe PDF | View/Open |
32465b.pdf Restricted Access | Published version | 561.45 kB | Adobe PDF | View/Open Request a copy |
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