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Guest Editorial Special Issue on Recent Advances in Theory, Methodology, and Applications of Imbalanced Learning

Xue, J-H; Ma, Z; Roveri, M; Japkowicz, N; (2020) Guest Editorial Special Issue on Recent Advances in Theory, Methodology, and Applications of Imbalanced Learning. IEEE Transactions on Neural Networks and Learning Systems , 31 (8) pp. 2688-2690. 10.1109/tnnls.2020.3003494. Green open access

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Abstract

Imbalanced learning is a challenging task in machine learning, faced by practitioners, and intensively investigated by researchers from a wide range of communities. However, as pointed out in the book titled “ Imbalanced Learning: Foundations, Algorithms, and Applications ” and collectively authored by experts in the field, many if not most of the approaches to imbalanced learning are heuristic and ad hoc in nature, hence leaving many questions unanswered. To fill this gap, the aim of this Special Issue is to collect recent research works that focus on the theory, methodology, and applications of imbalanced learning. After carefully reviewing a large number of submissions, we selected 15 works to be included in this Special Issue. These works can be roughly categorized into three types: deep-learning-based methods (6), methods based on other machine-learning paradigms (7), and empirical comparative studies (2).

Type: Article
Title: Guest Editorial Special Issue on Recent Advances in Theory, Methodology, and Applications of Imbalanced Learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tnnls.2020.3003494
Publisher version: https://doi.org/10.1109/tnnls.2020.3003494
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10106965
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