CIEL 2017 - 2017 IEEE Symposium on Computational Intelligence and Ensemble Learning (IEEE CIEL'2017)

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 (also known as the no free lunch theorem) 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 this symposium is to bring together researchers and practitioners who are working in the overlapping fields of ensemble methods and computational intelligence. Papers dealing with theory, algorithms, analysis, and applications of ensemble of computational intelligence methods are sought for this symposium.
Topics
Topics covered by the CIEL 2017 include, but are not limited to, the following:
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 of fuzzy models
Fuzzy ensemble classifiers and fuzzy ensemble predictors (Type-1 and Type-2)
Fuzzy ensemble feature selection/dimensionality reduction
Aggregation operators for fuzzy ensemble methods
Rough Set based ensemble clustering and classification
Ensemble of neural networks
Ensemble of neural classifier and clustering systems
Ensemble of neural feature selection algorithms
Properties of neural ensembles
Ensemble methods such as boosting, bagging, random forests, multiple classifier systems, mixture of experts, and multiple kernels
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