LPRCI 2017 - Workshop on Learning in the Presence of Class Imbalance and Concept Drift

With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Applications in various domains such as risk management, anomaly detection, fraud detection, software engineering, social media mining, and recommender systems are affected by both class imbalance and concept drift. Class imbalance happens when the data categories are not equally represented, i.e., at least one category is minority compared to other categories. It can cause learning bias towards the majority class and poor generalization. Concept drift is a change in the underlying distribution of the problem, and is a significant issue specially when learning from data streams. It requires learners to be adaptive to dynamic changes.
Class imbalance and concept drift can significantly hinder predictive performance, and the problem becomes particularly challenging when they occur simultaneously. This challenge arises from the fact that one problem can affect the treatment of the other. For example, drift detection algorithms based on the traditional classification error may be sensitive to the imbalanced degree and become less effective; and class imbalance techniques need to be adaptive to changing imbalance rates, otherwise the class receiving the preferential treatment may not be the correct minority class at the current moment. Therefore, the mutual effect of class imbalance and concept drift should be considered during algorithm design.
The aim of this workshop is to bring together researchers from the areas of class imbalance learning and concept drift in order to encourage discussions and new collaborations on solving the combined issue of class imbalance and concept drift. In order to advance the state-of-the-art on the combined issue, it is important to also advance the state-of-the art in each individual area. Therefore, this workshop encourages submissions not only on the combined issue, but also on these two areas themselves. This workshop will provide a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning and concept drift.
The topics of interest of this workshop include (but not limited to) the following:
Research topics related to the combined issues of class imbalance and concept drift:
Concept drift detection in imbalanced data streams.
New data-level and algorithm-level approaches to dealing with class imbalance in non-stationary environments.
Semi-supervised learning and active learning approaches to dealing with imbalanced data streams.
Adaptive ensemble approaches for imbalanced data streams.
Performance evaluation on imbalanced data streams in incremental and online learning scenarios.
Case studies and real-world applications dealing with both class imbalance and concept drift.
Research topics related to class imbalanced learning:
Data-level and algorithm-level techniques for imbalanced data.
Ensemble learning approaches for imbalanced data.
Cost-sensitive and cost-free learning approaches.
Imbalanced data with multiple classes or multiple labels.
Semi-supervised class imbalance learning.
Case studies and real-world applications dealing with class imbalanced data.
Research topics related to learning in the presence of concept drift:
Passive and active approaches to dealing with concept drift.
Concept drift detection methods.
Chunk-based and online learning approaches for non-stationary environments.
Approaches to dealing with recurring concepts.
Adaptive ensemble approaches.
Semi-supervised learning in non-stationary environments.
Case studies and real-world applications involving concept drift.