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LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test

Zhu, R; Wang, Z; Ma, Z; Wang, G; Xue, JH; (2018) LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test. Pattern Recognition Letters , 116 pp. 36-42. 10.1016/j.patrec.2018.09.012.

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Abstract

In this paper, we introduce a new likelihood ratio imbalance degree (LRID) to measure the class-imbalance extent of multi-class data. Imbalance ratio (IR) is usually used to measure class-imbalance extent in imbalanced learning problems. However, IR cannot capture the detailed information in the class distribution of multi-class data, because it only utilises the information of the largest majority class and the smallest minority class. Imbalance degree (ID) has been proposed to solve the problem of IR for multi-class data. However, we note that improper use of distance metric in ID can have harmful effect on the results. In addition, ID assumes that data with more minority classes are more imbalanced than data with less minority classes, which is not always true in practice. Thus ID cannot provide reliable measurement when the assumption is violated. In this paper, we propose a new metric based on the likelihood-ratio test, LRID, to provide a more reliable measurement of class-imbalance extent for multi-class data. Experiments on both simulated and real data show that LRID is competitive with IR and ID, and can reduce the negative correlation with F1 scores by up to 0.55.

Type: Article
Title: LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test
DOI: 10.1016/j.patrec.2018.09.012
Publisher version: https://doi.org/10.1016/j.patrec.2018.09.012
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.
Keywords: Imbalanced learning, Imbalance degree, Likelihood ratio, Class distribution
UCL classification: UCL > Provost and Vice Provost Offices
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: http://discovery.ucl.ac.uk/id/eprint/10058073
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