508 citations · 1.1k across the 13 of their papers we have counts for
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Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization
Alejandro Rosales-Pérez, Salvador García, Francisco Herrera
Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class;…
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification
Chongsheng Zhang, Paolo Soda, Jingjun Bi +3
In predictive tasks, real-world datasets often present different degrees of imbalanced (i.e., long-tailed or skewed) distributions. While the majority (the head) classes have suffi…
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…
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…
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…