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2020 | OriginalPaper | Buchkapitel

Synthetic Oversampling of Multi-label Data Based on Local Label Distribution

verfasst von : Bin Liu, Grigorios Tsoumakas

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multi-label sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare sub-concepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

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Titel: Synthetic Oversampling of Multi-label Data Based on Local Label Distribution
verfasst von: Bin Liu
Grigorios Tsoumakas
Verlag: Springer International Publishing
Buch: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-030-46146-1

Electronic ISBN: 978-3-030-46147-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-46147-8_11

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