Outliers Detection in Multi-label Datasets

Marilyn Bello*, Gonzalo Nápoles, Rafael Morera, Koen Vanhoof, Rafael Bello

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Soft Computing - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
EditorsLourdes Martínez-Villaseñor, Hiram Ponce, Oscar Herrera-Alcántara, Félix A. Castro-Espinoza
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030608835
Publication statusPublished - 2020
Event19th Mexican International Conference on Artificial Intelligence, MICAI 2020 - Mexico City, Mexico
Duration: 12 Oct 202017 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12468 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th Mexican International Conference on Artificial Intelligence, MICAI 2020
CityMexico City


  • Knowledge discovery
  • Multi-label datasets
  • Outlier detection
  • Outlier generation
  • Rough set theory


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