After the successful celebration of three previous editions under the frame of the Conference of the Spanish Association of Artificial Intelligence (CAEPIA'13, CAEPIA'15 and CAEPIA'16), the IV Symposium on Information Fusion and Ensemble Learning intends to establish a meeting point for all researchers with interest in the field of information fusion and ensembles, which is a key part of machine learning.

The symposium will welcome all contributions (both theoretical and applied) centered on the combination of simple machine learning algorithms to solve complex problems: this includes the management of several data sources, training schemes, combination of results or any other kind of fusion of information required inside an automatic system.

We hope FINO’18 to provide a stimulating and fruitful forum for presenting and discussing the latest works and advances on information fusion and ensemble learning.

Chairs

• Emilio Corchado, University of Salamanca, Email: escorchado@usal.es
• Mikel Galar, Public University of Navarra, Email: mikel.galar@unavarra.es
• Bruno Baruque, Burgos University, Email: bbaruque@ubu.es
• Alberto Fernández, University of Granada, Email: alberto@decsai.ugr.es

Topics

Topics of interest include, but are not limited to, the following:

• Ensemble Methodologies: Strategies and Techniques.
• Homogeneous and Heterogeneous Ensembles.
• Methods for ensemble learning: Boosting, Bagging, Random Forest, etc.
• Ensembles for classification, prediction, clustering, feature selection, etc.
• Definitions and measures of diversity.
• Methods for creating diversity in model generation.
• Relations between ensemble diversity and performance.
• Strategies for decision fusion.
• Evaluation of ensemble performance and comparison with other approaches.
• Software for ensembles and information fusion.
• Real world problem applications of ensembles.
• Fusion of One-class classifiers.
• Decomposition of multi-class problems: One-vs-One, One-vs-All, ECOC, etc.
• Ensembles for multi-instance or multi-label problems.
• Ensembles for dealing with noise problems, unbalanced classes and transfer learning.
• Design of information fusion strategies for distributed systems and Big Data.

Program Committee

Researcher	Institution
Juan Manuel Corchado	University of Salamanca, Spain
Ana Belén Gil González	University of Salamanca, Spain
Ángel Arroyo	University of Burgos, Spain
Belén Vaquerizo García	University of Burgos, Spain
Javier Sedano	Castilla y León Technological Institute, Spain
Jesús Ángel Román Gallego	University of Salamanca, Spain
José Luis Calvo Rolle	University of Coruña, Spain
José Luis Casteleiro Roca	University of Coruña, Spain
Leticia Curiel	University of Burgos, Spain
Pedro Antonio Hernández Ramos	University of Salamanca, Spain
Hector Quintián	University of Salamanca, Spain
Francisco Herrera	University of Granada, Spain
Salvador García	University of Granada, Spain
Julián Luengo	University of Burgos, Spain
Edurne Barrenechea	Public University of Navarra, Spain
Luciano Sánchez	University of Oviedo, Spain
Oscar Cordón	University of Granada, Spain
Oriol Pujol	University of Barcelona, Spain
Sergio Escalera	University of Barcelona, Spain
Daniel Hernández-Lobato	Autonomous University of Madrid, Spain
Aníbal Ramón Figueiras-Vidal	Carlos III University of Madrid, Spain
Araceli Sanchís	Carlos III University of Madrid, Spain
Agapito Ismael Ledezma	Carlos III University of Madrid, Spain
Jesús Mª Pérez	University of the Basque Country, Spain
José Antonio Sanz	Public University of Navarra, Spain
Santiago Porras	University of Burgos, Spain