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Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

conference contribution
posted on 14.02.2018, 15:07 by Shuo Wang, Leandro L Minku, Nitesh Chawla, Xin Yao
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.

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Citation

Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17), 2017

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Source

International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia

Version

VoR (Version of Record)

Published in

Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

Publisher

International Joint Conferences on Artificial Intelligence

isbn

978-0-9992411-0-3

Copyright date

2017

Publisher version

https://www.ijcai.org/proceedings/2017/

Notes

The file associated with this record is under a permanent embargo in accordance with the publisher's policy. The full text may be available through the publisher links provided above.

Temporal coverage: start date

19/07/2017

Language

en

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