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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

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.
Big Data
Parallelization of data mining algorithms for their adaptation to the processing of large volumes of data.
Ensemble Construction
This research line consists in designing new algorithms for construction strong classifiers from the combination of a set of weak classifiers.
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 modeling and Virtual Reality for the diffusion of Historical-Artistic and Archaeological Heritage
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.
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.

◉ Selected publications

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

2017

Sáiz-Manzanares, María Consuelo; Marticorena-Sánchez, Raúl; García-Osorio, César; Díez-Pastor, José Francisco

How Do B-Learning and Learning Patterns Influence Learning Outcomes? Journal Article

Frontiers in Psychology, 8 , pp. 745, 2017, ISSN: 1664-1078.

Abstract | Links | BibTeX

2016

Arnaiz-González, Álvar; Blachnik, Marcin; Kordos, Mirosław; García-Osorio, César

Fusion of instance selection methods in regression tasks Journal Article

Information Fusion, 30 , pp. 69 - 79, 2016, ISSN: 1566-2535.

Abstract | Links | BibTeX

Arnaiz-González, Álvar; Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César

Instance selection of linear complexity for big data Journal Article

Knowledge-Based Systems, 107 , pp. 83–95, 2016, ISSN: 0950-7051.

Abstract | Links | BibTeX

2015

Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César; Kuncheva, Ludmila I

Random Balance: Ensembles of variable priors classifiers for imbalanced data Journal Article

Knowledge-Based Systems, 85 , pp. 96-111, 2015, ISSN: 0950-7051.

Abstract | Links | BibTeX

Díez-Pastor, José Francisco; Rodríguez, Juan José; García-Osorio, César; Kuncheva, Ludmila I

Diversity techniques improve the performance of the best imbalance learning ensembles Journal Article

Information Sciences, 325 , pp. 98 - 117, 2015, ISSN: 0020-0255.

Abstract | Links | BibTeX

◉ Current projects

The following is our most recent funded research project, to see the full list of the funded projects in which we have participated, follow the link Research projects .

Ensemble algorithms for problem with multiple outputs. New developments and applications.
Referencia del proyecto: TIN2015-67534-P
Entidad / Administración financiadora: Ministerio de Economía y competitividad.
Importe (en euros): 65.703,00 €
Duración: Desde enero del 2016 hasta diciembre del 2019
Investigadores participantes (señalar IP): Dr. César Ignacio García Osorio*, Dr. Juan José Rodríguez Diez, Jesús Maudes Raedo, Andres Bustillo Iglesias, José Francisco Díez Pastor, Calor López Nozal, Raúl Marticorena Sánchez, Carlos Pardo Aguilar, Álvar Arnaiz González
Tipo de proyecto: Proyecto *    Contrato _
Descriptores:
Códigos UNESCO: 1203.04 Inteligencia Artificial, 1209.03 Análisis de Datos, 2
Áreas Tecnológicas: D-01 Inteligencia Artificial, D-09 Sistemas expertos, D-14 Procesamiento de Información,
Códigos CNAE: 7230 Proceso de datos, 7240 Actividades relacionadas con bases de datos, 7260 Otras actividades relacionadas con la informática

◉ The team



Juan José Rodríguez Diez


César Ignacio García Osorio


Andrés Bustillo Iglesias


Jesús Maudes Raedo


Carlos López Nozal


Raúl Marticorena Sánchez


José Francisco Diez Pastor


Carlos Pardo Aguilar


Álvar Arnaiz González


Nicolás García Pedrajas


Rodolfo E. Haber Guerra


Colin Fyfe