EAIS 2022 Special Session on Active Learning for Concept and Feature Drift Detection
Aims and Scope:
In the Deep Learning (DL) hyperconnected era, classifiers consume labels and labelled data at unprecedented rates. While computational power is no more an issue in model training, the time, cost, or human supervision required to produce high quality labelled data seriously hinders the maximum size of the available labelled datasets and hence the extensibility of DL in many challenging domains. To tackle the label-scarcity problem, Transfer Learning (TL) and Active Learning (AL) have both been exploited, the former using pre-trained models from a different domain and the latter choosing the best subset of instances to be labelled. Specifically, in the case of streaming data, labels may not be available for each instance of the stream (or being very costly, or come too fast for a human expert), data may have a very short lifespan, batch methods are unenforceable and issues due to concept and feature drift, or model switch, may prevent the training to be effective. The aim of the special session is to host original papers and reviews on recent research advances and state‐of‐the‐art methods in the fields of Computational Intelligence, Machine Learning, Data Mining and Distributed Computing methodologies concerning TL and AL techniques for concept and feature drift detection on streaming data.

Relevant topics within this context include, but are not limited to:
Computational Intelligence
Machine learning and Deep Learning
Sparse Coding
Data Mining
Fuzzy and Neuro‐Fuzzy Systems
Probabilistic and statistical modelling
Active Learning
Transfer Learning
Cost-Sensitive Learning
Online Learning
Concept Drift Detection
Feature Drift Detection
Online Feature Learning/Extraction/Selection

Important Dates:
Paper submission: January 10, 2022
Notification of acceptance/rejection: February 19, 2022
Camera ready submission: March 20, 2022

Submission:
Submitted papers should not exceed 8 pages plus at most 2 pages overlength
Submissions of full papers are accepted online through EasyChair system
https://cyprusconferences.org/eais2022/index.php/submission/