269 citations · 591 across the 14 of their papers we have counts for
10 papers · 1 filter
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)
Juan Luis Suárez-Díaz, Salvador García, Francisco Herrera
Distance metric learning is a branch of machine learning that aims to learn distances from the data, which enhances the performance of similarity-based algorithms. This tutorial pr…
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…
DPASF: A Flink Library for Streaming Data preprocessing
Alejandro Alcalde-Barros, Diego García-Gil, Salvador García +1
Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. A…
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala
M. Cristina Heredia-Gómez, Salvador García, Pedro Antonio Gutiérrez +1
Ordinal Data are those where a natural order exist between the labels. The classification and pre-processing of this type of data is attracting more and more interest in the area o…
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
Sergio Ramírez-Gallego, Salvador García, Ning Xiong +1
With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms tha…
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
Anabel Gómez-Ríos, Siham Tabik, Julián Luengo +3
The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in…
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…