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The ADMIRABLE  — Advanced Data MIning Research And (Business intelligence | Bioinformatics | Big data) LEarning — research group main aim is the development of new ensemble algorithms and the application of data mining, data visualization and pattern matching techniques to diverse fields as bioinformatics, time series classification and high dimensional data analysis.

Among the main achievements of the researchers of the group is the development of several new ensemble construction algorithms: Rotation Forest, Disturbing Neighbours, Nonlinear Boosting Projections, Random Balance, …, that have aroused the interest of the data mining community.

Its members are in close relationship with research groups of other universities, currently collaborating with groups from the universities of Córdoba and Valladolid. The researchers of the group have international connections too. They have done research works while visiting other universities such as Stockholm University in Sweden, University of Gales and West Scotland University in United Kingdom, and Tsinghua University in China.

Its research interests are among others: Artificial Intelligence, Data Mining, Multi-Dimensional Data Visualization, Ensemble Construction, Bioinformatics, Regressors Ensembles, Instance Selection, Feature Selection, Decision Trees.

The group is recognized by the regional government of Castile and Leon as a Consolidated Research Unit.

◉ Research lines

Ensemble Construction
This research line consists in designing new algorithms for construction strong classifiers from the combination of a set of weak classifiers.
Data Mining
Data mining has its roots in artificial intelligence and statistics, is the process of extracting hidden patterns from data solving forecasting, classification and clustering problems.
Instance and attribute selection
Reduce the number of instances and characteristics of a data set to accelerate processing without affecting the information obtained. Extend the methods used in the area of classification to others like regression and multi-label classification.
Big Data
Parallelization of data mining algorithms for their adaptation to the processing of large volumes of data.
Bioinformatics
Bioinformatics is the application of data mining and data analysis techniques to manage and analyse biological data. Common problems in bioinformatics include sequence aligning and gene prediction.
Data mining applications in Software Engineering
Reduce software maintenance costs by improving the quality of software processes and products through the application of data mining.
Applied Visualization Techniques
Designing new methods for the graphic representation of Multi-dimensional data in order to obtain a clearer idea of its structure, but also 3D modelling and Virtual Reality for the diffusion of Historical-Artistic and Archaeological Heritage

◉ Current projects

The following is the list of our current research projects, to see the full list, go to the Research projects page.

Sistemas dinámicos inteligentes centrados en el usuario para la Prevención de Riesgos Laborales (HUManAID-ORP) 🔗


Referencia del proyecto: TED2021-129485B-C43
Entidad / Administración financiadora: Ministerio de Ciencia e Innovación, Fondos NextGenerationEU.
Importe (en euros): 139.610,00 €
Duración: Desde el 1 de diciembre del 2022 hasta el 30 de diciembre del 2024
Nº de Investigadores: 15
Investigador principal: Dr. Álvar Arnaiz González y Dr. Andrés Bustillo Iglesias
Descriptores:
ANEP: INF.- Ciencias de la Computación y Tec. Informát.
NABS: 13.2.- I+D de Ingeniería
FORD: 1.02.- Computación y Ciencias de la Información
UNESCO: 120304
ÁREA: Computer Science
CATEGORÍA: Computer Science Applications
(más información sobre el proyecto aquí)

Uso de imágenes SENTINEL para la monitorización de prácticas agrícolas y su contribución a la iniciativa “4 por 1000” de incremento de carbono orgánico en el suelo (SEN4CFARMING) 🔗


Referencia del proyecto: TED2021-131638B-I00
Entidad / Administración financiadora: Ministerio de Ciencia e Innovación, Fondos NextGenerationEU.
Importe (en euros): 105.570,00 €
Duración: Desde el 1/12/2022 hasta el 30/11/2024
Nº de Investigadores: 11
Investigador principal: Dr. José Francisco Diez Pastor y Dr. Pedro Latorre Carmona
Descriptores:
ANEP: INF.- Ciencias de la Computación y Tec. Informát.
NABS: 13.2.- I+D de Ingeniería
FORD: 1.02.- Computación y Ciencias de la Información
UNESCO: 120304
ÁREA: Computer Science
CATEGORÍA: Computer Science Applications
(más información sobre el proyecto aquí)

Machine learning with scarcely labeled data for Industry 4.0 🔗


Referencia del proyecto: PID2020-119894GB-I00
Entidad / Administración financiadora: Ministerio de Ciencia e Innovación, Proyectos I+D+i 2020
Importe (en euros): 49.852,00 €
Duración: 2021-2024
Nº de Investigadores: 19
Investigador principal: Dr. César Ignacio García Osorio y Dr. Andrés Bustillo Iglesias
Descriptores:
ANEP: INF.- Ciencias de la Computación y Tec. Informát.
NABS: 13.2.- I+D de Ingeniería
FORD: 1.02.- Computación y Ciencias de la Información
UNESCO: 120304
ÁREA: Computer Science
CATEGORÍA: Computer Science Applications
(más información sobre el proyecto aquí)

◉ The team

César Ignacio García Osorio

Juan José Rodríguez Diez

Andrés Bustillo Iglesias

Jesús Maudes Raedo

Carlos López Nozal

Raúl Marticorena Sánchez

José Francisco Diez Pastor

Carlos Pardo Aguilar

Álvar Arnaiz González

Mario Juez Gil

Antonio Canepa Oneto

José A. Barbero Aparicio

César Represa Pérez

Pedro Latorre Carmona

David Checa Cruz

Francisco J. González Moya

José Luis Garrido Labrador

Kim Martinez García

Alicia Olivares Gil

José Miguel Ramírez Sanz

Ismael Ramos Pérez

Ana Serrano Mamolar

Jose Alberto Maestro Prieto

David García García

Sandra Rodríguez Arribas

María Esther Delgado Cubo

Henar Guillén Sanz

Inés Miguel Alonso

Bruno Rodríguez García
Laura Galindo González

Rodrigo Pascual García

Nicolás García Pedrajas

Rodolfo E. Haber Guerra

Colin Fyfe

◉ Selected publications

The following is a short list of our latest publications, to see the full list, go to the Publications page.

2024

Garrido-Labrador, José Luis; Serrano-Mamolar, Ana; Maudes-Raedo, Jesús; Rodríguez, Juan J.; García-Osorio, César

Ensemble methods and semi-supervised learning for information fusion: A review and future research directions Journal Article

In: Information Fusion, vol. 107, 2024, ISSN: 1566-2535.

Links | BibTeX

Guillen-Sanz, Henar; Checa, David; Miguel-Alonso, Ines; Bustillo, Andres

A systematic review of wearable biosensor usage in immersive virtual reality experiences Journal Article

In: Virtual Reality, vol. 28, no. 2, 2024, ISSN: 1434-9957.

Abstract | Links | BibTeX

Guillen-Sanz, Henar; Checa, David; Miguel-Alonso, Ines; Bustillo, Andres

A systematic review of wearable biosensor usage in immersive virtual reality experiences Journal Article

In: Virtual Reality, vol. 28, no. 2, 2024, ISSN: 1434-9957.

Abstract | Links | BibTeX

Rodríguez-García, Bruno; Guillen-Sanz, Henar; Checa, David; Bustillo, Andrés

A systematic review of virtual 3D reconstructions of Cultural Heritage in immersive Virtual Reality Journal Article

In: Multimedia Tools and Applications, 2024, ISSN: 1573-7721.

Abstract | Links | BibTeX

Kuncheva, Ludmila I.; Garrido-Labrador, José Luis; Ramos-Pérez, Ismael; Hennessey, Samuel L.; Rodríguez, Juan J.

Semi-supervised classification with pairwise constraints: A case study on animal identification from video Journal Article

In: Information Fusion, vol. 104, 2024, ISSN: 1566-2535.

Links | BibTeX

Ramos-Pérez, Ismael; Barbero-Aparicio, José Antonio; Canepa-Oneto, Antonio; Arnaiz-González, Álvar; Maudes-Raedo, Jesús

An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data Journal Article

In: Information, vol. 15, no. 4, 2024, ISSN: 2078-2489.

Abstract | Links | BibTeX

Maestro-Prieto, Jose Alberto; Ramírez-Sanz, José Miguel; Andrés Bustillo, and Juan José Rodriguez-Díez

Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults Journal Article

In: Applied Intelligence, 2024, ISSN: 1573-7497.

Abstract | Links | BibTeX

Martin-Melero, Íñigo; Serrano-Mamolar, Ana; Rodríguez-Diez, Juan J.

Evaluation of Semi-Supervised Machine Learning applied to Affective State Detection Bachelor Thesis

2024.

Links | BibTeX

Guillen-Sanz, Henar; Checa, David; Miguel-Alonso, Inés; Bustillo, Andrés

A systematic review of wearable biosensor usage in immersive virtual reality experiences Journal Article

In: Virtual Reality, vol. 28, no. 74, 2024, ISSN: 1434-9957.

Abstract | Links | BibTeX

Miguel-Alonso, Ines; Checa, David; Guillen-Sanz, Henar; Bustillo, Andres

Evaluation of the novelty effect in immersive Virtual Reality learning experiences Journal Article

In: Virtual Reality, vol. 28, no. 1, 2024, ISSN: 1434-9957.

Abstract | Links | BibTeX

Garrido-Labrador, José Luis; Serrano-Mamolar, Ana; Maudes-Raedo, Jesús; Rodríguez, Juan José; García-Osorio, César

Ensemble methods and semi-supervised learning for information fusion: A review and future research directions Journal Article

In: Information Fusion, vol. 107, 2024.

Abstract | Links | BibTeX

Miguel-Alonso, Ines; Checa, David; Guillen-Sanz, Henar; Bustillo, Andres

Evaluation of the novelty effect in immersive Virtual Reality learning experiences Journal Article

In: Virtual Reality, vol. 28, no. 27, 2024, ISSN: 1434-9957.

Abstract | Links | BibTeX

Martinez, Kim; Checa, David; Bustillo, Andres

Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice Journal Article

In: Electronics, vol. 13, iss. 281, no. 2, pp. 281, 2024.

Abstract | Links | BibTeX

Marticorena-Sánchez, Raúl; López-Nozal, Carlos; Serrano-Mamolar, Ana; Olivares-Gil, Alicia

UBUMonitor: Desktop application for visual e-learning student clustering with Moodle Journal Article

In: SoftwareX, vol. 26, pp. 101727, 2024, ISSN: 2352-7110.

Abstract | Links | BibTeX

Barbero-Aparicio, José A.; Olivares-Gil, Alicia; Rodríguez, Juan J.; García-Osorio, César; Díez-Pastor, José F.

Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques Journal Article

In: Information Fusion, vol. 102, pp. 102035, 2024, ISSN: 1566-2535.

Abstract | Links | BibTeX