IJCAI 2017年关于存在阶级不平衡和概念漂移的学习研讨会论文集（LPCICD '17）
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
摘要：
随着机器学习算法在现实世界中的广泛应用，类不平衡和概念漂移已经成为关键的学习问题。当数据类别没有被平等地表示时，就会发生类不平衡，即，与其他类别相比，至少有一个类别属于少数群体。它会导致学习偏向多数类和泛化能力差。概念漂移是问题的潜在分布的变化，特别是在从数据流中学习时是一个重要的问题。它要求学习者能够适应动态变化。类别不平衡和概念漂移会严重阻碍预测性能，当它们同时发生时，问题变得特别具有挑战性。这一挑战的产生是因为一个问题可能影响到对另一个问题的处理。例如，基于传统分类误差的漂移检测算法可能对不平衡程度敏感并且变得不太有效;并且类别不平衡技术需要适应变化的不平衡率，否则接收优先处理的类别可能不是当前时刻正确的少数类别。因此，在算法设计时，应考虑类不平衡和概念漂移的相互影响。本次研讨会的目的是汇集来自班级不平衡学习和概念漂移领域的研究人员，以鼓励讨论和新的合作，解决班级不平衡和概念漂移的综合问题。它为国际研究人员和从业者提供了一个论坛，分享和讨论他们的原创作品，以解决班级不平衡学习，概念漂移以及班级不平衡和概念漂移的综合问题中的新挑战和研究问题。会议记录包括关于这些主题的8篇论文。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
