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Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines.

, , , and . Inf. Fusion, (2020)

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Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization., , , and . CoRR, (2018)Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem., , , , , and . Expert Syst. Appl., 37 (6): 4050-4057 (2010)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)Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets., , , and . Appl. Soft Comput., (2014)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)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)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)Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass., , , , and . Comput. Chem. Eng., (2017)