Novel fuzzy clustering-based undersampling framework for class imbalance problem

Author:

Pratap VibhaORCID,Singh Amit Prakash

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Safety, Risk, Reliability and Quality

Reference40 articles.

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2. Amin A, Anwar S, Adnan A, Nawaz M, Howard N, Qadir J, Hawalah A, Hussain A (2016) Comparing oversampling techniques to handle the class imbalance problem: A customer churn prediction case study. IEEE Access 4:7940–7957

3. Barua S, Islam MM, Yao X, Murase K (2012) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425

4. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6(1):20–29

5. Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):91–203

Cited by 2 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. Comparison of Undersampling Methods for Imbalanced Credit Card Fraud Dataset;2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE);2023-11-23

2. A Novel Clustering-Based Three Level Under-Sampling Algorithm For Class Imbalance Problem;J APPL SCI ENG;2023

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