IEEE Symposium on Evolving and Autonomous Learning Systems (IEEE EALS)

The EALS 2021 Symposium will be a focal point for presentation of the recent advanced research results and industrial applications in the area of evolving and autonomously learning systems. The role of autonomous learning from (big) data (streams) is growing with the exponential explosion of amounts, complexity and hetero-genuous nature of the data we are living through. The traditional methods of machine learning, probabilistic and even computational intelligence techniques such as neural networks and fuzzy sets and systems require in practice a lot of handcrafting, make restrictive assumptions and are often not directly applicable to dynamically changing, evolving data with non-stationary properties, of hetero-genuous nature (mixing signals, image/video, text), categorical variables, etc. Extracting autonomously interpretable models which are not fixed, but dynamically evolving is a key challenges to be addressed. The Symposium has established track record and aims to keep and build upon this with the current event, EALS 2021.


New Adaptive and Evolving Learning Methods:

  • Evolving in Dynamic Environments
  • Drift and Shift in Data Streams
  • Self-monitoring Evolving Systems
  • Evolving Decision Systems / Evolving Perceptions
  • Self-organising Systems/ Evolving Neuro-fuzzy Systems
  • Neural Networks with Evolving Structure
  • Non-stationary Time Series Prediction with ES
  • Automatic Novelty Detection in Evolving Systems
  • Stability, Robustness, Unlearning Effects
  • Structure Flexibility and Robustness in Evolving Systems
  • Evolving Fuzzy Clustering Methods
  • Evolving Fuzzy Rule-based Classifiers
  • Evolving Intelligent Systems for Time Series Prediction
  • Evolving Intelligent System State Monitoring and Prognostics
  • Evolving Intelligent Controllers
  • Evolving Fuzzy Decision Support Systems
  • Evolving Consumer Behaviour Models

Real-world Application

  • Robotics and Control Systems
  • Industrial Applications
  • Data Mining and Knowledge Discovery
  • Intelligent Transport
  • Bio-Informatics
  • Defense

Symposium Chairs

Nikola Kasabov
Auckland University of Technology, New Zealand