CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS @IJCNN/WCCI 2016
             
CONCEPT DRIFT, DOMAIN ADAPTATION &  LEARNING IN DYNAMIC ENVIRONMENTS
TPC
             
Special Session on "CONCEPT DRIFT, DOMAIN ADAPTATION & LEARNING IN DYNAMIC ENVIRONMENTS" @ WCCI 2016

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 IEEE World Congress of Computational Intelligence (WCCI 2016), Vancouver Canada in July, 25-29 2016 http://wcci2016.org

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
  • Methodologies, algorithms and techniques for learning in dynamic/non-stationary environments
  • Incremental learning, lifelong learning, cumulative learning
  • Domain adaptation and dataset-shift, covariate-shift adaptation
  • Semi-supervised learning methods for handling concept-drift
  • Ensemble methods for learning under concept drift
  • Learning under concept drift and class unbalance
  • Change-detection tests and anomaly-detection algorithms
  • Algorithms for information mining in nonstationary datastreams
  • Applications that call for learning in dynamic/non-stationary environments, and for incremental learning, such as:
    • Adaptive classifiers for concept drift and recurring concepts
    • Intelligent systems operating in dynamic/non-stationary environments
    • Intelligent embedded and cyber-physical systems
  • Applications that call for change and anomaly detection, such as:
    • fault detection
    • fraud detection
    • network-intrusion detection and security
    • intelligent sensor networks
  • Cognitive-inspired approaches to adaptation and learning
  • Development of test-sets benchmarks for evaluating algorithms learning in non-stationary/dynamic environments
  • Issues relevant to above mentioned or related fields

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

Download the Call for Papers in pdf format