CIEL 2013

2013 IEEE Symposium on 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 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.


The symposium topics include, but are not limited to:


Ensemble of evolutionary algorithms

Parameter and operator ensembles for evolutionary algorithms


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.

4.   Hybridization of computational intelligence ensemble systems

5.  Applications of ensemble of computational intelligence methods in any field


Keynote, Tutorial and Panel Sessions

Please forward your proposals with detailed abstract and bio-sketches of the speakers to Symposium Co-Chairs and SSCI Keynote-Tutorial Chair, Dr S Das.

Special Sessions

Please forward your special session proposals to Symposium Co-Chairs.

Symposium Co-Chairs

Nikhil R Pal, ISI, Kolkatta, India

Xin Yao, University of Birmingham, UK

P. N. Suganthan,  Nanyang Technological University, Singapore

Program Committee (provisional, to be confirmed)

Dr Ashish Anand, India

Dr. J Balasubramaniam, India

Dr S. Baskar, India

Dr. Jyh-Yeong Chang, Taiwan
Dr. Shyi-Ming Chen, Taiwan
Dr. I-F Chung, Taiwan

Bartosz Krawczyk, Poland

Dr. Swagatam Das, India

Dr Pradip Ghanty, India

Dr. Amit Konar, India

Dr. Arijit Laha, India

Dr Jane J Liang, China

Dr. Q-K Pan, China

Dr G. Pugalenthi, Kingdom of Saudi Arabia

Dr A. K. Qin, France

Dr. B-Y Qu, China

Dr Rammohan Mallipeddi, Korea

Dr Ke Tang, China

Dr M Fatih Tasgetiren, Turkey

Dr Shizheng Zhao, Singapore