IEEE CIDUE' 2018 aims to bring together all researchers, practitioners and students to present and discuss the latest advances in the field of Computational Intelligence (CI), such as neural networks and learning algorithms, fuzzy systems, evolutionary computation and other emerging techniques for dealing with uncertainties encountered in evolutionary optimization, machine learning and data mining.
- Evolutionary computation in dynamic and uncertain environments
- Use of surrogates for single and multi-objective optimization
- Search for robust solutions over space and time
- Dynamic single and multi-objective optimization
- Handling noisy fitness functions
- Learning and adaptation in evolutionary computation
- Learning in non-stationary and uncertain environments
- Incremental and lifelong learning
- Online and interactive learning
- Dealing with catastrophic forgetting
- Active and autonomous learning in changing environments
- Ensemble techniques
- Multi-objective learning
- Learning from severely unbalanced data, including multiclass unbalanced data.
- Mining of temporal patterns
- Temporal data mining techniques and methodologies
- Incorporating domain knowledge for efficient temporal data mining
- Scalability of temporal data mining algorithms
- Mining of temporal data on the web
- Hybrid methodologies for dealing with uncertainties, interactions of evolution and learning in changing environments, benchmarks, performance measures, and real-world applications
De Montfort University, UK.
Rowan University, USA.
Nottingham Trent University, UK.
University of Surrey, UK.
(To be announced)