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A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/archpsyc.1983.01790060110016openalex-snapshot
The Mini-Mental State Examination
inferredcites →https://doi.org/10.1002/eap.2043openalex-snapshot
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inferredcites →https://doi.org/10.1007/978-3-319-71249-9_46openalex-snapshot
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inferredcites →https://doi.org/10.1007/s13748-016-0094-0openalex-snapshot
Learning from imbalanced data: open challenges and future directions
inferredcites →https://doi.org/10.1016/0098-3004(84)90020-7openalex-snapshot
FCM: The fuzzy c-means clustering algorithm
inferredcites →https://doi.org/10.1016/j.asoc.2014.04.001openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.eswa.2006.08.020openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.fss.2014.01.015openalex-snapshot
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data
inferredcites →https://doi.org/10.1016/j.ins.2013.07.007openalex-snapshot
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
inferredcites →https://doi.org/10.1016/j.knosys.2013.09.023openalex-snapshot
Repeated double cross-validation for choosing a single solution in evolutionary multi-objective fuzzy classifier design
inferredcites →https://doi.org/10.1016/j.neunet.2018.05.011openalex-snapshot
Class-specific extreme learning machine for handling binary class imbalance problem
inferredcites →https://doi.org/10.1109/ijcnn.2016.7727769openalex-snapshot
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inferredcites →https://doi.org/10.1109/tcbb.2018.2827029openalex-snapshot
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inferredcites →https://doi.org/10.1109/tcyb.2019.2913572openalex-snapshot
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inferredcites →https://doi.org/10.1109/tfuzz.2010.2091961openalex-snapshot
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inferredcites →https://doi.org/10.1109/tfuzz.2016.2604003openalex-snapshot
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inferredcites →https://doi.org/10.1109/tfuzz.2016.2637405openalex-snapshot
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inferredcites →https://doi.org/10.1109/tfuzz.2017.2717803openalex-snapshot
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inferredcites →https://doi.org/10.1109/tfuzz.2018.2824763openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnnls.2017.2732482openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnnls.2017.2751612openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnnls.2018.2886341openalex-snapshot
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inferredcites →https://doi.org/10.1109/tnsre.2017.2748388openalex-snapshot
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inferredcites →https://doi.org/10.1109/tpami.2018.2832629openalex-snapshot
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inferredcites →https://doi.org/10.1109/tpwrd.2005.855423openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsmc.1985.6313399openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsmc.2017.2759090openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsmcb.2005.844594openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-snapshot
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inferredcites →https://doi.org/10.1142/s0218001409007326openalex-snapshot
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
inferredcites →https://doi.org/10.1186/s40537-019-0192-5openalex-snapshot
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inferredcites →https://doi.org/10.1613/jair.1199openalex-snapshot
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
inferredcites →https://doi.org/10.9781/ijimai.2016.419openalex-snapshot
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inferred← citeshttps://doi.org/10.2139/ssrn.4803360openalex-extracted
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inferred← citeshttps://doi.org/10.1007/978-981-16-8225-4_32openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10278-024-01229-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s44196-025-01112-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109786openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111498openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112010openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108685openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.109975openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120677openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120966openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.inffus.2022.12.014openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.inffus.2023.101845openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ins.2024.120976openalex-snapshot
A dynamic broad TSK fuzzy classifier based on iterative learning on progressively rebalanced data
inferred← citeshttps://doi.org/10.1016/j.ins.2024.121359openalex-snapshot
A fully interpretable stacking fuzzy classifier with stochastic configuration-based learning for high-dimensional data
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122142openalex-snapshot
A calibrated fully interpretable fuzzy classifier via Vapnik–Chervonenkis-dimension minimization learning
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122892openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcyb.2020.3034792openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcyb.2022.3164900openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcyb.2022.3181193openalex-snapshot
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