Online neural network model for non-stationary and imbalanced data stream classification-Reference-Cited by-同舟云学术

Online neural network model for non-stationary and imbalanced data stream classification

Published:2013-06-30 Issue:1 Volume:5 Page:51-62
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Ghazikhani Adel,Monsefi Reza,Sadoghi Yazdi Hadi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-013-0180-6.pdf

Reference44 articles.

1. Gama J (2010) Knowledge discovery from data streams. Chapman & Hall/CRC Press, Boca Raton

2. Masud MM (2009) Adaptive classification of scarcely labeled and evolving data streams. Texas, Dallas

3. Klinkenberg R, Joachims T (2000) Detecting concept drift with support vector machines. In: Paper presented at the 17th International conference on machine learning, San Mateo

4. Sun J, Li H (2011) Dynamic financial distress prediction using instance selection for the disposal of concept drift. Expert Syst Appl 38(3):2566–2576

5. Martínez-Rego D, Pérez-Sánchez B, Fontenla-Romero O, Alonso-Betanzos A (2011) A robust incremental learning method for non-stationary environments. Neurocomputing 74(11):1800–1808

Cited by 62 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. Intensive Class Imbalance Learning in Drifting Data Streams;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

2. Text Stream Classification: Literature Review and Current Trends;2023 International Conference on Computational Science and Computational Intelligence (CSCI);2023-12-13

3. Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams;Knowledge-Based Systems;2023-12

4. An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing;International Journal of Intelligent Systems;2023-10-20

5. A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework;Machine Learning;2023-06-29