2016年IEEE计算智能和集成学习研讨会 2016 IEEE Symposium on Computational Intelligence and Ensemble Learning IEEE CIEL'2016

活动简介

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 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.

征稿信息

征稿范围

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
Fuzzy ensemble clustering
Fuzzy ensemble classifiers and fuzzy ensemble predictors
Fuzzy 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
Applications of ensemble of computational intelligence methods in any field