CIDUE 2017 - 2017 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE' 2017)

IEEE CIDUE' 2017 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.
Topics
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