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Special Issue 'Hybrid and Ensemble Methods in Machine Learning'

June 30, 2012 - 12:30
Journal of Universal Computer Science
Edwin Lughofer

Hybrid and ensemble methods in machine learning have gained a great attention of scientific community over the last several years. Multiple learning models have been theoretically and empirically shown to provide significantly better performance than their single base models. Ensemble algorithms and hybrid methods of reasoning have found their application in various real word problems ranging from person recognition through medical diagnosis and text classification to financial forecasting. The HEMML 2012 Special Issue of Journal of Universal Computer Science, is devoted to both hybrid and ensemble methods and their application to classification, prediction, and clustering problems. The impact factor of J.UCS is 0.669, the 5-year impact factor 0.788 (2010). All issues and papers are assigned a digital object identifier (DOI). After the success of the 4th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2012) and especially, the Special Session on Multiple Model Approach to Machine Learning (MMAML 2012) at this, we want to offer another opportunity for researchers and practitioners to extend their work and publish recent advances in this area. The scope of the special issue includes the following topics:

• Theoretical framework for ensemble methods
• Ensemble learning algorithms: bagging, boosting, stacking, etc.
• Ensemble methods in clustering
• Dealing with large volumes of data and lack of adequate data
• Subsampling and feature selection in multiple model machine learning
• Diversity, accuracy, interpretability, and stability issues
• Homogeneous and heterogeneous ensembles
• Hybrid methods in prediction and classification
• Ensemble methods for dealing with concept drift
• Incremental, evolving, and online ensemble learning
• Mining data streams using ensemble methods
• Multi-objective ensemble learning
• Ensemble methods in agent and multi-agent systems
• Implementations of ensemble learning algorithms
• Assessment and statistical analysis of ensemble models
• Applications of ensemble methods in business, engineering, medicine, etc.