Imbalanced networked multi-label classification with active learning

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

3 Scopus Citations
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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 9th IEEE International Conference on Big Knowledge (ICBK 2018)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages290-297
ISBN (electronic)978-1-5386-9125-0
Publication statusPublished - Nov 2018

Publication series

NameProceedings - IEEE International Conference on Big Knowledge, ICBK

Conference

Title9th IEEE International Conference on Big Knowledge, ICBK 2018
PlaceSingapore
CitySingapore
Period17 - 18 November 2018

Abstract

With the rapid development of social networks, the networked multi-label classification algorithms have gained wide attention. The existing networked multi-label classification algorithms mostly only consider the homogeneity or heterogeneity of the network without taking the imbalance of the network into account, and this is actually pretty common in real network environments, which deserves more attention. Moreover, the selection strategy of training set is very critical for multi-label classification algorithm, because it will directly affect both the parameter updating inside the classifier and the precision of the classifier. The application of active learning to the selection of training set can effectively improve the precision of the classifier. Similarly, the application of imbalanced data processing strategies to the selection of training sets also makes classifiers more suitable for imbalanced data networks. Thereout, we propose an algorithm BSHD (Block Sampling with selecting the Highest Degree nodes), which is an active learning based imbalanced networked multi-label classification algorithm. In this algorithm, we divide the network according to the edge density and utilize the oversampling and undersampling to dispose each block. Then we select the nodes with the highest degree from each block to form the training set. Experimental results show that our proposed BSHD outperforms other state-of-arts approaches.

Research Area(s)

  • Active learning, Imbalanced data, Multi-label classification algorithm, Oversampling, Undersampling

Citation Format(s)

Imbalanced networked multi-label classification with active learning. / Zhang, Ruilong; Li, Lei; Zhang, Yuhong et al.
Proceedings - 9th IEEE International Conference on Big Knowledge (ICBK 2018). Institute of Electrical and Electronics Engineers, Inc., 2018. p. 290-297 00046 (Proceedings - IEEE International Conference on Big Knowledge, ICBK).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review