A general framework for multi-label learning towards class correlations and class imbalance-Reference-Cited by-同舟云学术

A general framework for multi-label learning towards class correlations and class imbalance

Published:2019-04-04 Issue:2 Volume:23 Page:371-383
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Peng Yue¹,Huang Edward²,Chen Gang¹,Wang Chongjun¹,Xie Junyuan¹

Affiliation:

1. National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, Jiangsu, China

2. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

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5. Z.A. Daniels and D.N. Metaxas, Addressing imbalance in multi-label classification using structured hellinger forests, in: AAAI, 2017, pp. 1826–1832.

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