COST 2018 - International Workshop on Cost-Sensitive Learning

The research topics of interest to COST'2018 workshop include (but are not limited to) the following:
Foundations of cost- and utility-based learning
Probabilistic and statistical models
New knowledge discovery theories and models
Deep learning in the context of cost-sensitive learning
Handling cost-sensitive big data
Learning with non i.i.d. data
Relations between cost/utility-based learning and data pre-processing/post-processing
Sampling approaches
Feature selection and feature transformation
Evaluation in cost-sensitive learning
Knowledge discovery and machine learning in cost and utility-based tasks
Classification, ordinal classification
Regression
Data streams and time series forecasting
Clustering
Outlier detection
Adaptive learning and algorithm-level approaches
Multi-label, multi-instance, sequence and association rules mining
Active learning
Spatial and spatio-temporal learning
Applications of cost and utility-based learning
Budgeted applications
Fraud detection (e.g. finance, credit and online banking)
Anomaly detection (e.g. industry, intrusion detection)
Health applications
Environmental applications (e.g. meteorology, biology)
Social media applications (e.g. popularity prediction, recommender systems)
Real world applications (e.g. oil spill detection)
Case studies