1 code implementation • 9 Apr 2024 • Francisco Herrera, Daniel Jiménez-López, Alberto Argente-Garrido, Nuria Rodríguez-Barroso, Cristina Zuheros, Ignacio Aguilera-Martos, Beatriz Bello, Mario García-Márquez, M. Victoria Luzón
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount.
no code implementations • 3 Apr 2024 • Alberto Argente-Garrido, Cristina Zuheros, M. Victoria Luzón, Francisco Herrera
In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation.
no code implementations • 3 Apr 2024 • Iván Sevillano-García, Julián Luengo, Francisco Herrera
As Artificial Intelligence systems become integral across domains, the demand for explainability grows.
no code implementations • 22 Mar 2024 • Cristina Zuheros, David Herrera-Poyatos, Rosana Montes, Francisco Herrera
This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions.
1 code implementation • 7 Feb 2024 • Rosana Montes, Ana M. Sanchez, Pedro Villar, Francisco Herrera
Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it.
no code implementations • 30 Oct 2023 • Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 26 Jul 2023 • Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task.
no code implementations • 2 May 2023 • Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh, Marcos López de Prado, Enrique Herrera-Viedma, Francisco Herrera
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective.
no code implementations • 20 Feb 2023 • Javier Poyatos, Daniel Molina, Aitor Martínez, Javier Del Ser, Francisco Herrera
MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models.
1 code implementation • 11 Nov 2022 • Iván Sevillano-García, Julián Luengo-Martín, Francisco Herrera
There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations.
no code implementations • 5 Sep 2022 • Yang Nan, Javier Del Ser, Zeyu Tang, Peng Tang, Xiaodan Xing, Yingying Fang, Francisco Herrera, Witold Pedrycz, Simon Walsh, Guang Yang
especially for cohorts with different lung diseases.
2 code implementations • 7 Jun 2022 • Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera
The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others.
1 code implementation • 20 May 2022 • Javier Del Ser, Alejandro Barredo-Arrieta, Natalia Díaz-Rodríguez, Francisco Herrera, Andreas Holzinger
To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i. e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual.
no code implementations • 21 Apr 2022 • Alejandro Rosales-Pérez, Salvador García, Francisco Herrera
The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters.
1 code implementation • 8 Feb 2022 • Javier Poyatos, Daniel Molina, Aritz. D. Martinez, Javier Del Ser, Francisco Herrera
Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network.
no code implementations • 20 Jan 2022 • Nuria Rodríguez-Barroso, Daniel Jiménez López, M. Victoria Luzón, Francisco Herrera, Eugenio Martínez-Cámara
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence.
no code implementations • 17 Jan 2022 • Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor, Angel Alberich-Bayarri, Marion I. Menzel, Sean Walsh, Wim Vos, Nina Flerin, Jean-Paul Charbonnier, Eva van Rikxoort, Avishek Chatterjee, Henry Woodruff, Philippe Lambin, Leonor Cerdá-Alberich, Luis Martí-Bonmatí, Francisco Herrera, Guang Yang
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
1 code implementation • 11 Nov 2021 • David Charte, Francisco Charte, Francisco Herrera
They can be applied as a preprocessing stage for a binary classification problem.
1 code implementation • 8 Sep 2021 • Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
This algorithm filters and relabels instances of the training set based on the predictions and their probabilities made by the backbone neural network during the training process.
no code implementations • 26 May 2021 • Jacinto Carrasco, Irina Markova, David López, Ignacio Aguilera, Diego García, Marta García-Barzana, Manuel Arias-Rodil, Julián Luengo, Francisco Herrera
The research in anomaly detection lacks a unified definition of what represents an anomalous instance.
no code implementations • 25 May 2021 • Francisco Pérez-Hernández, José Rodríguez-Ortega, Yassir Benhammou, Francisco Herrera, Siham Tabik
However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i. e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large.
2 code implementations • 24 Apr 2021 • Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez, Gianni Franchi, Ivan Donadello, Siham Tabik, David Filliat, Policarpo Cruz, Rosana Montes, Francisco Herrera
We tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph.
no code implementations • 23 Apr 2021 • Roberto Olmos, Siham Tabik, Francisco Perez-Hernandez, Alberto Lamas, Francisco Herrera
Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable.
no code implementations • 8 Oct 2020 • Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Jesus L. Lobo, Francisco Herrera
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously.
1 code implementation • 21 Sep 2020 • Jesus L. Lobo, Javier Del Ser, Eneko Osaba, Albert Bifet, Francisco Herrera
Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.
no code implementations • 9 Aug 2020 • Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera
In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.
no code implementations • 4 Aug 2020 • Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong, Francisco Herrera
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making.
1 code implementation • 31 Jul 2020 • Cristina Zuheros, Eugenio Martínez-Cámara, Enrique Herrera-Viedma, Francisco Herrera
We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • 29 Jul 2020 • Nuria Rodríguez-Barroso, Eugenio Martínez-Cámara, M. Victoria Luzón, Francisco Herrera
We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model.
1 code implementation • 15 Jul 2020 • José Daniel Pascual-Triana, David Charte, Marta Andrés Arroyo, Alberto Fernández, Francisco Herrera
However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance.
no code implementations • 2 Jul 2020 • Nuria Rodríguez-Barroso, Goran Stipcich, Daniel Jiménez-López, José Antonio Ruiz-Millán, Eugenio Martínez-Cámara, Gerardo González-Seco, M. Victoria Luzón, Miguel Ángel Veganzones, Francisco Herrera
The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use.
1 code implementation • 21 May 2020 • David Charte, Francisco Charte, María J. del Jesus, Francisco Herrera
All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
1 code implementation • 8 May 2020 • David Charte, Francisco Charte, María J. del Jesus, Francisco Herrera
Autoencoders are techniques for data representation learning based on artificial neural networks.
no code implementations • 19 Apr 2020 • Antonio LaTorre, Daniel Molina, Eneko Osaba, Javier Del Ser, Francisco Herrera
In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task.
no code implementations • 24 Mar 2020 • Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo, Javier Del Ser, Francisco Herrera
Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration.
1 code implementation • 5 Mar 2020 • Sergio González, Salvador García, Sheng-Tun Li, Robert John, Francisco Herrera
This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).
no code implementations • 25 Feb 2020 • Aritz D. Martinez, Eneko Osaba, Javier Del Ser, Francisco Herrera
A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality , and the intertask relationships found and exploited over the search process.
no code implementations • 19 Feb 2020 • Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera
From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior.
no code implementations • 6 Feb 2020 • Jesus L. Lobo, Javier Del Ser, Francisco Herrera
A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms).
no code implementations • 16 Jan 2020 • Diego García-Gil, Salvador García, Ning Xiong, Francisco Herrera
Ensembles have shown to be able to successfully address imbalanced data problems.
1 code implementation • 22 Oct 2019 • Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera
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.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 18 Jul 2019 • Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik, Luigi Troiano, Roberto Tagliaferri, Francisco Herrera
The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video.
no code implementations • 14 Dec 2018 • Juan Luis Suárez-Díaz, Salvador García, Francisco Herrera
This tutorial provides a theoretical background and foundations on this topic and a comprehensive experimental analysis of the most-known algorithms.
no code implementations • 29 Nov 2018 • David Charte, Francisco Charte, Salvador García, Francisco Herrera
This field is subdivided into multiple areas, among which the best known are supervised learning (e. g. classification and regression) and unsupervised learning (e. g. clustering and association rules).
no code implementations • 23 Oct 2018 • M. Cristina Heredia-Gómez, Salvador García, Pedro Antonio Gutiérrez, Francisco Herrera
The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems.
1 code implementation • 14 Oct 2018 • Alejandro Alcalde-Barros, Diego García-Gil, Salvador García, Francisco Herrera
Data preprocessing techniques are devoted to correct or alleviate errors in data.
1 code implementation • 16 Apr 2018 • Sergio Ramírez-Gallego, Salvador García, Ning Xiong, Francisco Herrera
Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).
no code implementations • 27 Mar 2018 • Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera
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 the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups.
no code implementations • 14 Feb 2018 • Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification.
no code implementations • 14 Feb 2018 • Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed.
1 code implementation • 10 Feb 2018 • Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years.
1 code implementation • 4 Jan 2018 • David Charte, Francisco Charte, Salvador García, María J. del Jesus, Francisco Herrera
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model.
1 code implementation • IEEE 2017 2017 • Sergio Ramírez-Gallego, Héctor Mouriño-Talín, David Martínez-Rego, Verónica Bolón-Canedo, José Manuel Benítez, Amparo Alonso-Betanzos, Francisco Herrera
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.
no code implementations • 3 Jun 2017 • Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura, Javier Cabello, Francisco Herrera
There is a growing demand for accurate high-resolution land cover maps in many fields, e. g., in land-use planning and biodiversity conservation.
no code implementations • 6 Apr 2017 • Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used.
no code implementations • 21 Mar 2017 • Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, Jose M. Benitez, Francisco Herrera
In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction.
1 code implementation • 16 Feb 2017 • Roberto Olmos, Siham Tabik, Francisco Herrera
Current surveillance and control systems still require human supervision and intervention.
no code implementations • 12 Feb 2017 • Andrés Herrera-Poyatos, Francisco Herrera
The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation.
no code implementations • 13 Oct 2016 • Sergio Ramírez-Gallego, Héctor Mouriño-Talín, David Martínez-Rego, Verónica Bolón-Canedo, José Manuel Benítez, Amparo Alonso-Betanzos, Francisco Herrera
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.