Machine Learning Ensemble Methods and Applications
The 11th International Conference on Machine Learning and Applications(
ICMLA 2012 . )
December 12-15, 2012, Boca Raton, Florida, USA
Note: The submission Deadline for papers is August 6th, 2012.
Organisers:
Chair: Dr. Wenjia Wang
School of Computing Sciences
University of East Anglia,
Norwich, UK
Important dates
Submission:
August 6th, 2012
Notification:
March
Sept. 7th, 2012
Camera-Ready:
October 1st, 2012
Conference:
Dec. 12-25, 2012
Contact Information
Dr. Wenjia Wang
School of Computing Sciences, UEA, Norwich, NR4 7TJ, UK.
Email: wjw@uea.ac.uk
Tel: +44 (0)1603 592577
Fax: +44 (0)1603 593345
Background and Objectives
As rapidly increasing quantity and complexity of real-world data have overwhelmed the existing individual machine learning methods and techniques to produce satisfactory performance when working alone, more and more attention has been paid to machine learning ensemble methods that utilize the collective power of many individual models to achieve better and more importantly highly reliable performance.
Various ensemble methods and algorithms have been developed and applied to many real-world problems over last decade. However, there are still some fundamental issues that are not well understood and there is a need for further in-depth investigation to answer key questions in ensemble approaches.
For example, what factors affect the performance of an ensemble and in what extent they do? Which diversity measures should be used when assessing the difference among the models in an ensemble? What methodology and procedure should be used for constructing an effective ensemble in a more efficient manner? What are the constraints and challenges of the ensemble approach?
This special session is intended to bring the researchers from a widened spectrum to report their progress made in the last few years and to discuss their experiences in researching and using ensemble methods in various applications.
Topics
The major topics of interest for this special session include,
but are not limited to:
Ensemble methodology, strategies and techniques
Homogeneous and heterogeneous ensembles
Methods for buidling ensembles,
e.g. Boosting, Random Forest, etc.
Ensemble for classification, prediction,
clustering, feature selection
Diversity definition and measurement
Strategies and techniques for generating diverse models
Relationships between diversity and ensemble's accuracy
Decision fusion strategies
Evaluation of ensemble performance and comparisons with other approaches
Ensemble software development
Applications.
Special session papers are treated the same as regular conference papers, and will
be included in the conference proceedings.
Technical Committee
Chair: Wenjia Wang ( University of East Anglia, UK)
Daniel C. Neagu (University of Bradford, UK), Philip Yu (UIC, USA), Ning Zhong (Maebashi Institute of Technology, Japan), Yinhui Jun (University of Manchester, UK), Tony Bagnall, Gavin Cawley, Bea de la Iglesia (UEA, UK)