269 citations · 510 across the 6 of their papers we have counts for
6 papers
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
David Charte, Francisco Charte, María J. del Jesus +1
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate w…
A Showcase of the Use of Autoencoders in Feature Learning Applications
David Charte, Francisco Charte, María J. del Jesus +1
Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding spec…
Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets
Francisco Charte, Antonio J. Rivera, María J. del Jesus +1
Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly,…
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization
Francisco Charte, Antonio J. Rivera, María J. del Jesus +1
The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification. A handful of multilabel resampling…
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository
Francisco Charte, Antonio J. Rivera, David Charte +2
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always…
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
David Charte, Francisco Charte, Salvador García +2
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these…