CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS @ IJCNN 2018
             
CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS
TPC
             
Special Session on "CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS" @ IJCNN 2018

One of the fundamental goals in computational intelligence is to achieve brain-like intelligence, a remarkable property of which is the ability to incrementally learn from noisy and incomplete data, and ability to adapt to changing environments. The special session aims at presenting novel approaches to incremental learning and adaptation to dynamic environments both from the more traditional and theoretical perspective of computational intelligence and from the more practical and application-oriented one.

This Special Session aspires at building a bridge between academic and industrial research, providing a forum for researchers in this area to exchange new ideas with each other, as well as with the rest of the neural network & computational intelligence community.

The Special Session will be held within the WCCI 2018, Rio de Janeiro in July, 8-13 2018 WCCI website

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
  • Methods and algorithms for learning in dynamic/non-stationary environments
  • Transfer Learning and Domain Adaptation
  • Incremental learning, lifelong learning, cumulative learning
  • Online learning and stream mining algorithms
  • Change and covariate-shift adaptation
  • Semi-supervised learning methods for nonstationary environments
  • Ensemble methods for learning in nonstationary environments
  • Learning under concept drift and class imbalance
  • Learning recurrent concepts
  • Change-detection and anomaly-detection algorithms
  • Information-mining algorithms in nonstationary data streams
  • Cognitive-inspired approaches for adaptation and learning
  • Applications that call for learning in dynamic/non-stationary environments, or change/anomaly detection, such as:
    • adaptive classifiers for concept drift
    • adaptive/Intelligent systems
    • fraud detection
    • fault detection and diagnosis
    • network-intrusion detection and security
    • intelligent sensor networks
    • time series analysis
  • Development of datasets/benchmarks/standards for evaluating algorithms learning in non-stationary/dynamic environments
  • Adversarial machine learning

Keywords: Concept drift, nonstationary environment, change/anomaly detection, domain adaptation, incremental learning, data streams.