The impact of data difficulty factors on classification of imbalanced and concept drifting data streams
The impact of data difficulty factors on classification of imbalanced and concept drifting data streams
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DOI：
10.1007/s10115-021-01560-w
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发表时间：
2021-04-01
影响因子：
2.7
通讯作者：
Szumaczuk, Artur
中科院分区：
文献类型：
--
作者：
Brzezinski, Dariusz;Minku, Leandro L.;Szumaczuk, Artur
当从概念漂移数据流中学习分类器时，类不平衡会带来额外的挑战。现有的大多数工作集中在设计新的算法来处理全局不平衡比，并没有考虑其他数据的复杂性。对静态不平衡数据的独立研究强调了局部数据难度因素的影响作用，例如少数类分解和存在不安全类型的示例。尽管经常存在于现实世界的数据，概念漂移和本地数据难度因素之间的相互作用还没有被调查的概念漂移数据流。我们彻底研究这种相互作用的影响漂移不平衡流。为此，我们提出了一种新的类别不平衡问题的概念漂移分类。通过综合实验与合成和真实的数据流，我们研究的影响，概念漂移，全球类不平衡，本地数据的难度因素，以及它们的组合，预测的代表性在线分类。实验结果表明，新考虑的因素和它们的局部漂移，以及现有的分类器的反应，这些因素的影响很大。多个因素的组合对分类器来说是最具挑战性的。虽然现有的分类器部分能够应对全球类的不平衡，需要新的方法来解决不平衡的数据流所带来的挑战。
Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers' reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.