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Neurocomputing-CIEL 2014 : Computational Intelligence and Ensemble Learning


When N/A
Where N/A
Submission Deadline Feb 15, 2014
Notification Due Mar 31, 2014
Final Version Due Sep 30, 2014
Categories    ensemble learning   computational intelligenec   machine learning   classification

Call For Papers

Call for papers for a special issue of
An Elsevier Publication
“Computational Intelligence and Ensemble Learning "

Neurocomputing is planning a special issue devoted to Computational Intelligence and Ensemble Learning. Ensemble learning attempts to enhance the performance of systems (clustering, classification, prediction, feature selection, search, optimization, rule extraction, etc.) by using multiple models instead of using a single model. This approach is intuitively meaningful as a single model may not always be the best for solving a complex problem while multiple models are more likely to yield results better than each of the constituent models. Although in the past, ensemble methods have been mainly studied in the context of classification and time series prediction, recently they are being used in algorithms in other scenarios such as clustering, fuzzy systems, evolutionary algorithms, dimensionality reduction and so on.
The aim of the issue is to discuss the new theoretical trends and the applications of ensemble learning and related approaches using hybrid and bio-inspired approaches.
Topics are encouraged, but not limited to, the use of ensemble models to design and develop Intelligent Systems:
• Ensemble of evolutionary algorithms
• Parameter and operator ensembles for evolutionary algorithms
• Hyper-heuristics
• Portfolio of algorithms and multi-method search
• Ensemble of evolutionary algorithms for optimization scenarios such as multi-objective, combinatorial, constrained, etc.
• Hybridization of evolutionary algorithms with other search methods& ensemble methods
• Ensemble clustering
• Ensemble classifiers and fuzzy ensemble predictors
• Ensemble feature selection/dimensionality reduction
• Aggregation operators for fuzzy ensemble methods
• Rough Set based ensemble clustering and classification
• Type-2 Fuzzy ensemble clustering and classification
• Ensemble methods such as boosting, bagging, random forests, multiple classifier systems, mixture of experts, multiple kernels, etc.
• Ensemble methods for regression, classification, clustering, ranking, feature selection, prediction, etc.
• Issues such as selection of constituent models, fusion and diversity of models in an ensemble, etc.
• Hybridization of computational intelligence ensemble systems
• One-class classifier ensembles,
• Diversity measures and ensemble pruning
• Classifier ensemble techniques for data stream classification
• Applications of ensemble of computational intelligence methods in any field.
Manuscripts should be submitted electronically online at
The corresponding author will have to create a user profile if one has not been established before at Elsevier and choose the appropriate special issue acronym during submission.

Guest Editors
Prof. Xin Yao - University of Birmingham,
Dr. Michal Wozniak – Wroclaw University of Technology,

Deadline for Submission: February, 15, 2014

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