Call for Papers IJCNN 2015 Special Session "Ensemble Systems and Machine Learning"

Special Session for IEEE IJCNN 2015. 

Scope

Ensembles are essential tools for classification and prediction tasks in many real-world applications.  The past decade has witnessed a vast growth of the amount of machine learning methods using ensemble, becoming a popular approach due to their capabilities in handling many real world complex problems and to the good results and analysis presented in many recently published papers. Results show that ensembles of classifiers and forecasting models can achieve better accuracy than single ones. However, ensembles face many challenges in obtaining diverse and optimized base models, as well as obtaining good fusion algorithms.

This special session of IJCNN 2015 will cover all aspects of the latest achievements in ensemble systems particularly machine learning based ensembles and their applications.

Topics for submission include, but are not limited to:

• Ensemble techniques and algorithms
• Multiple experts
• Committee of experts
• Fusion of classifiers
• Fusion of forecasting models
• Optimization techniques for multiple classifiers
• Applications of ensemble and fusion techniques

etc.

Biographic Information:

Marley Vellasco received the BSc and MSc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, in 1984 and 1987, respectively, and the PhD degree in Computer Science from the University College London (UCL) in 1992. Dr. Vellasco is currently Head of the Electrical Engineering Department of PUC-Rio and of the Computational Intelligence and Robotics Laboratory (LIRA) of PUC-Rio. She is the author of four books and more than 45 papers in professional journals, 300 papers in conference proceedings and 15 book chapters in the area of soft computing and machine learning. Her research interests include Neural Networks, Fuzzy Logic, Neuro-Fuzzy Systems, Neuro-Evolutionary models, Robotics and Intelligent Agents, applied to decision support systems, pattern classification, time-series forecasting, control, optimization and Data Mining.

Teresa B. Ludermir received the Ph.D. degree in Artificial Neural Networks in 1990 from Imperial College, University of London, UK. She is a Full Professor at the Center of Informatics, Universidade Federal de Pernambuco, Brazil. She has published over a 200 articles in scientific journals and conferences, three books in Neural Networks and organized two of the Brazilian Symposium on Neural Networks. She is one of the editors-in-Chief of the International Journal of Computation Intelligence and Applications. Her main interests are Machine Learning, Meta-Learning, Hybrid Intelligent Systems, and Time Series Forecasting.