79 citations · 182 across the 6 of their papers we have counts for
6 papers
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…
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…