Conditional semi‐fuzzy c ‐means clustering for imbalanced dataset-Reference-Cited by-同舟云学术

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Conditional semi‐fuzzy c ‐means clustering for imbalanced dataset

Published:2020-07-27 Issue:11 Volume:14 Page:2343-2355
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Gao Yunlong¹,Yang Chengyu²,Lin Kuo‐Yi²,Pan Jinyan³,Li Li²

Affiliation:

1. School of Aerospace Engineering, Xiamen UniversityXiamenPeople's Republic of China

2. College of Electronics and Information Engineering, Tongji UniversityShanghaiPeople's Republic of China

3. Information Engineering College, Jimei UniversityXiamenPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.0253

Reference45 articles.

1. Statistical pattern recognition: a review

2. Dynamic structural neural network

3. Unscented Kalman filter for learning of a solar dryer and a greenhouse;Rubio J.J.;J. Intell. Fuzzy Syst.,2019

4. Multivariate image segmentation with cluster size insensitive Fuzzy C-means

5. Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers

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1. EM-IFCM: Fuzzy c-means clustering algorithm based on edge modification for imbalanced data;Information Sciences;2024-02

2. Multiscale Laplacian graph kernel features combined with tree deep convolutional neural network for the detection of ECG arrhythmia;Biomedical Signal Processing and Control;2022-07

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