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05/26/2023
mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms
Resampling algorithms are a useful approach to deal with imbalanced lear...
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01/15/2023
EvoAAA: An evolutionary methodology for automated autoencoder architecture search
Machine learning models work better when curated features are provided t...
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11/11/2021
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
Available data in machine learning applications is becoming increasingly...
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05/21/2020
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
In many machine learning tasks, learning a good representation of the da...
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05/08/2020
A Showcase of the Use of Autoencoders in Feature Learning Applications
Autoencoders are techniques for data representation learning based on ar...
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11/29/2018
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
Machine learning is a field which studies how machines can alter and ada...
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02/23/2018
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction
High dimensionality, i.e. data having a large number of variables, tends...
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02/14/2018
Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets
Multilabel classification is an emergent data mining task with a broad r...
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02/14/2018
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization
The learning from imbalanced data is a deeply studied problem in standar...
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02/10/2018
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository
New proposals in the field of multi-label learning algorithms have been ...
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01/04/2018