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 manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.

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 covered by the CIEL 2018 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