269 citations · 581 across the 13 of their papers we have counts for
13 papers
Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples
Francisco Charte, Miguel Ángel Dávila, María Dolores Pérez-Godoy +1
Predictive models trained on imbalanced data tend to produce biased results. This problem is exacerbated when there is not just one output label, but a set of them. This is the cas…
PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders
David de la Rosa, Antonio J Rivera, María J del Jesus +1
Photo-trapping cameras are widely employed for wildlife monitoring. Those cameras take photographs when motion is detected to capture images where animals appear. A significant por…
mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms
Antonio J. Rivera, Miguel A. Dávila, David Elizondo +2
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as…
EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search
Francisco Charte, Antonio J. Rivera, Francisco Martínez +1
Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a prop…
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
David Charte, Francisco Charte, Francisco Herrera
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to m…
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…
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…