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Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization., , , and . CoRR, (2018)Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines., , , and . Inf. Fusion, (2020)ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders., , , and . Inf. Sci., (2021)Dealing with difficult minority labels in imbalanced mutilabel data sets., , , and . Neurocomputing, (2019)PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders., , , and . CoRR, (2023)Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions., , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (12): 9549-9560 (2022)NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images., , , , , , and . IWANN (2), volume 14135 of Lecture Notes in Computer Science, page 39-51. Springer, (2023)Improving Multi-label Classifiers via Label Reduction with Association Rules., , , and . HAIS (2), volume 7209 of Lecture Notes in Computer Science, page 188-199. Springer, (2012)Automatic Time Series Forecasting with GRNN: A Comparison with Other Models., , , and . IWANN (1), volume 11506 of Lecture Notes in Computer Science, page 198-209. Springer, (2019)MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation., , , and . Knowl. Based Syst., (2015)