posted by user: michalwozniak || 1222 views || tracked by 4 users: [display]

LD2 2018 : LEARNING FROM DIFFICULT DATA

FacebookTwitterLinkedInGoogle

Link: http://www.smc2018.org/approved-special-sessions/c14-learning-from-difficult-data/
 
When Oct 7, 2018 - Oct 10, 2018
Where Miyazaki, Japan
Submission Deadline Mar 31, 2018
Notification Due Jun 1, 2018
Final Version Due Jul 20, 2018
Categories    machine learnig   pattern classification   imbalanced data   difficult data
 

Call For Papers

Difficulties embedded within characteristics of real-life data pose various challenges to contemporary machine learning algorithms. The performance of learning algorithms may be strongly impaired by adverse data characteristics, such as data velocity, imbalanced distributions, high number of classes, high-dimensional feature spaces, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or concept drift (i.e., parameter change of the probabilistic characteristics describing data), to enumerate only a few.
The main aim of this section is to bring together researchers and scientists from basic computing disciplines (computer science and mathemathics) and researchers from various application areas who are pioneering data analysis methods in sciences, as well as in humanitarian fields, to discuss problems and solutions in the area of data difficulties, to identify new issues, and to shape future directions for research.

The list of possible topics includes, but is not limited to:

class imbalanced learning
learning from data streams
learning in the presence of concept drift
learning with limited ground truth access
learning from high dimensional data
learning on the basis of limited data sets, including one-shot learning
instance and prototype selection
data imputation methods
case studies and real-world applications affected by data difficulties

Session Chairs
Bartosz Krawczyk (bkrawczyk@vcu.edu),
Virginia Commonwealth University, USA
Michal Wozniak (michal.wozniak@pwr.edu.pl),
Wroclaw University of Science and Technology, Poland

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
MLDM 2022   18th International Conference on Machine Learning and Data Mining
SENSORS Special Issue 2022   'Next Generation of Secure and Resilient Healthcare Data Processing'
JDSA SI 2021   Springer Journal Special Issue on Data Science for Next-Generation Recommender Systems
CTE 2022   10th Workshop on Cloud Technologies in Education
DLIS 2022   Deep Learning for IoT Security - Frontiers in Big Data Journal
SI-DAMLE 2022   Special Issue on Data Analytics and Machine Learning in Education
Special issue on Recommender systems 2021   Scopus/Springer Special issue: Data Science for Next-Generation Recommender Systems with International Journal of Data Science and Analytics
ADRW 2021   «Archives during rebellions and wars». From the age of Napoleon to the cyber war era
IJCAI 2022   31st International Joint Conference on Artificial Intelligence