Dr. Wenjia Wang
School of Computing Sciences
University of East Anglia,
Norwich, UK

Professor Derek Partridge
Dept. Computer Science
University of Exeter
Exeter, UK

Contact Information
Dr. Wenjia Wang
Email: wjw@cmp.uea.ac.uk
Tel: +44 (0)1603 592577
Fax: +44 (0)1603 593345

Scope

Ensemble in the context of machine learning is viewed as a paradigm in which multiple individual models (e.g. classifiers or predictors) are trained to learn a given task from data and then work collectively to produce a better overall decision for unseen instances through a decision fusion strategy. Ensemble approach has been popularised in recently years primarily due to the complexity and the sheer quantity of real world data, the unsatisfactory performance of individual techniques, and the ever improving computing power and facilities that make construction of ensembles easier, cheaper and quicker. To make an ensemble more effective, apart from relatively high accuracy, the individual models must be diverse from each other. However, diversity does not come naturally, and measuring diversity and producing a high level of diversity are not easy tasks.

This special session aims to provide an opportunity to bring the topic-related researchers together to review the l atest developments on ensemble methods and diversity measures, exchange and discuss new ideas for building effective ensembles.

Topics