Title: Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17),
Abstract: With the wide application of machine learning algorithms to the real world,
class imbalance and concept drift have become crucial learning issues. 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. It provides 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, concept drift, and
the combined issues of class imbalance and concept drift. The proceedings
include 8 papers on these topics.