Keynote Speaker

“Bayesian Networks Based Multi-dimensional Classification”
 
Prof. Pedro Larrañaga, Department of Artificial Intelligence, Technical University of Madrid, Spain
 

pedro-larrañaga-nov-2014

Abstract

 In this talk, the problem of multi-dimensional classification (a generalization of multi-label classification) will be approached with Bayesian networks. Multi-dimensional Bayesian network classifiers will be introduced and, under this paradigm, we will see that the classification problem can be formulated as the finding of the most probable explanation. As this is an NP-complete problem, in order to alleviate its computational burden, a special type of multi-dimensional Bayesian network classifiers called class-bridge decomposable will be introduced. Methods for learning this type of models from data, both based on score+search and on estimating the Markov blanket of each class variable, will be presented. Some variants of the general framework that consider the use of chain classifiers, or super-classes or even tratable (from the point of view of inference) models will be explained. How this kind of models can be updated in data stream settings will be also discussed. Finally, the application of the methodology real word problems in neuroscience, industry 4.0 and sports will be presented.

Bio

Pedro Larranaga is Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country (“excellence award”). Before moving to UPM, his academic career has been developed at the University of the Basque Country (UPV-EHU) at several faculty ranks: Assistant Professor (1985-1998), Associate Professor (1998-2004) and Full Professor (2004-2007). He earned the habilitation qualification for Full Professor in 2003.

His research interests are primarily in the areas of probabilistic graphical models, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry4.0 and sports. He has published more than 200 papers in impact factor journals and has supervised 25 PhD theses. He is fellow of the European Association for Artificial Intelligence since 2012. He has been awared the 2013 Spanish National Prize in Computer Science and the prize of the Spanish Association for Artificial Intelligence in 2018.