A density weighted fuzzy outlier clustering approach for class imbalanced learning
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/content/pdf/10.1007/s00521-020-04747-4.pdf
Reference35 articles.
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2. Barua S, Islam MM, Yao X, Murase K (2014) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425. https://doi.org/10.1109/TKDE.2012.232
3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer, Boston, pp 95–154
4. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) Dbsmote: density-based synthetic minority over-sampling technique. Appl Intell 36(3):664–684. https://doi.org/10.1007/s10489-011-0287-y
5. Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200–210. https://doi.org/10.1016/j.eswa.2012.07.021
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