508 citations · 1.1k across the 13 of their papers we have counts for
16 papers
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…
Recent Trends in the Use of Statistical Tests for Comparing Swarm and Evolutionary Computing Algorithms: Practical Guidelines and a Critical Review
J. Carrasco, S. García, M. M. Rueda +2
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliabl…
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)
Daniel Molina, Javier Poyatos, Javier Del Ser +3
In recent years, bio-inspired optimization methods, which mimic biological processes to solve complex problems, have gained popularity in recent literature. The proliferation of pr…
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser +9
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this t…
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…
Monotonic classification: an overview on algorithms, performance measures and data sets
José-Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk +2
Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bank…
Label Noise Filtering Techniques to Improve Monotonic Classification
José-Ramón Cano, Julián Luengo, Salvador García
The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the prob…
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…
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…
Enabling Smart Data: Noise filtering in Big Data classification
Diego García-Gil, Julián Luengo, Salvador García +1
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale…
On the use of convolutional neural networks for robust classification of multiple fingerprint captures
Daniel Peralta, Isaac Triguero, Salvador García +3
Fingerprint classification is one of the most common approaches to accelerate the identification in large databases of fingerprints. Fingerprints are grouped into disjoint classes,…
JavaNPST: Nonparametric Statistical Tests in Java
J. Derrac, S. García, F. Herrera
Nonparametric statistical tests are useful procedures that can be applied in a wide range of situations, such as testing randomness or goodness of fit, one-sample, two-sample and m…