Opening Special Issues

Special Issue "Solving Complex Machine Learning Problems with Ensemble Methods"

Special Issue Editor(s)
Dr. Wenzhong Yang
Neuroscience pharmacology, University of Connecticut, USA
Email: wenzhongyang@gmail.com, wenzhong.z.yang@opwdd.ny.gov

Special Issue Information: By combining the decisions of several different predictors, ensemble methods provide appealing solutions to challenging problems in machine learning. These include for example dealing with learning under non-standard circumstances, i.e., when large volumes of data are available for induction, or when a data stream has to be classified under the phenomenon of concept drift. Similarly, ensemble methods can be used to tackle difficult problems related to multi-label classification, feature selection, or active learning. Although research in the field of ensemble learning has grown considerably in the recent years, the specific application of ensemble methods to the problems described is still in a very early stage. There are still many open issues and there remain challenges which may require interdisciplinary approaches. This special issue aims to gather research works in the area of ensemble methods to present the latest results obtained and the efforts of the community to address difficult machine learning problems.

Special Issue Topics:

• Large Scale Learning
• Multi-modal Learning
• Multi-Label Classification
• Data-stream classification and Concept Drift adaptation
• Multi-Dimensional Classification
• Feature Selection
• Active Learning
• Mining social networks
• Applications of Ensemble Methods
• Clustering Ensembles