Expanding MLkNN Using Extended Rough Set Theory

Gabriela Pérez*, Marilyn Bello, Gonzalo Nápoles, María Matilde García, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

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

Abstract

Multi-label classification refers to the problem of associating an object with multiple labels. This problem has been successfully addressed from the perspective of problem transformation and adaptation of algorithms. Multi-Label k-Nearest Neighbour (MLkNN) is a lazy learner that has reported excellent results, still there is room for improvements. In this paper we propose a modification to the MLkNN algorithm for the solution to problems of multi-label classification based on the Extended Rough Set Theory. More explicitly, the key modifications are focused in obtaining the relevance of the attributes when computing the distance between two instances, which are obtained using a heuristic search method and a target function based on the quality of the similarity. Experimental results using synthetic datasets have shown promising prediction rates. It is worth mentioning the ability of our proposal to deal with inconsistent scenarios, a main shortcoming present in most state-of-the-art multi-label classification algorithms.
Original languageEnglish
Title of host publicationProgress in Artificial Intelligence and Pattern Recognition
EditorsYanio Hernández Heredia, Vladimir Milián Núñez, José Ruiz Shulcloper
Place of PublicationCham
PublisherSpringer International Publishing
Pages247-254
Number of pages8
ISBN (Print)978-3-030-01132-1
Publication statusPublished - 2018
Externally publishedYes
Event6th International Workshop, IWAIPR 2018:International Workshop on Artificial Intelligence and Pattern Recognition -
Duration: 24 Sep 2018 → …

Conference

Conference6th International Workshop, IWAIPR 2018:International Workshop on Artificial Intelligence and Pattern Recognition
Period24/09/18 → …

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