269 citations · 347 across the 4 of their papers we have counts for
8 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…
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
David Charte, Francisco Charte, Salvador García +1
Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdiv…
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction
Francisco J. Pulgar, Francisco Charte, Antonio J. Rivera +1
High dimensionality, i.e. data having a large number of variables, tends to be a challenge for most machine learning tasks, including classification. A classifier usually builds a…
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…