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Multi-Imbalance: An open-source software for multi-class imbalance learning
Henan University, China.
Henan University, China.
Henan University, China.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-3460-2902
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2019 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 174, p. 137-143Article in journal (Refereed) Published
Abstract [en]

Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance.

Place, publisher, year, edition, pages
2019. Vol. 174, p. 137-143
Keywords [en]
Imbalanced data classification, Multi-class imbalance leaning, Classification (of information), Learning algorithms, Learning systems, Open systems, Class imbalance, Class imbalance learning, Imbalanced data, Multi-class imbalanced datum, Research problems, Source codes, Open source software
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Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-38243DOI: 10.1016/j.knosys.2019.03.001Scopus ID: 2-s2.0-85062978887OAI: oai:DiVA.org:ri-38243DiVA, id: diva2:1301497
Note

 Funding details: European Research Consortium for Informatics and Mathematics, ERCIM; Funding text 1: The research of Enislay Ramentol is funded by the European Research Consortium for Informatics and Mathematics (ERCIM), France Alain Bensoussan Fellowship Programme.

Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-07-01Bibliographically approved

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