Multi-view learning with fisher kernel and bi-bagging for imbalanced problem-Reference-Cited by-同舟云学术

Multi-view learning with fisher kernel and bi-bagging for imbalanced problem

Published:2019-03-09 Issue:8 Volume:49 Page:3109-3122
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Wang Zhe^ORCID,Zhu Yiwen,Chen Zhaozhi,Zhang Jing,Du Wenli

Funder

Natural Science Foundation of China

National Science Foundation of China for Distinguished Young Scholars

Shanghai Education Development Foundation and Shanghai Municipal Education Commission

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/content/pdf/10.1007/s10489-019-01428-1.pdf

Reference48 articles.

1. Akaho S (2006) A kernel method for canonical correlation analysis. arXiv: cs/0609071

2. Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17:255–287

3. Bach F, Lanckriet GR, Jordan MI (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: International conference on machine learning. ACM, pp 6–13

4. Bishop CM (2007) Pattern recognition and machine learning. Springer

5. Bryll R, Gutierrez-Osuna R, Quek F (2003) Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn 36(6):1291–1302

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-view cost-sensitive kernel learning for imbalanced classification problem;Neurocomputing;2023-10

2. Boost two-view learning-based method for label proportions problem;Applied Intelligence;2023-06-18

3. Mvqs: Robust Multi-View Instance-Level Cost-Sensitive Learning Method for Imbalanced Data Classification;2023

4. Feature Selection and Ensemble Learning Techniques in One-Class Classifiers: An Empirical Study of Two-Class Imbalanced Datasets;IEEE Access;2021

5. A similarity-based two-view multiple instance learning method for classification;Knowledge-Based Systems;2020-08