Abstract The rapid growth of the information technology accelerates organizations to generate vast volumes of high-velocity data streams. The concept drift is a crucial issue, and discovering the sequential patterns over data streams are more challenging. The ensemble classifiers incrementally learn the data for providing quick reaction to the concept drifts. The ensemble classifiers have to process both the gradual and sudden concept drifts that happen in the real-time data streams. Thus, a novel ensemble classifier is essential that significantly reacting to various types of concept drifts quickly and maintaining the classification accuracy. This work proposes the stream data mining on the fly using an adaptive online learning rule (SOAR) model to handle both the gradual and sudden pattern changes and improves mining accuracy. Adding the number of classifiers fails because the ensemble tends to include redundant classifiers instead of high-quality ones. Thus, the SOAR includes different diversity levels of classifiers in the ensemble to provide fast recovery from both the concept drifts. Moreover, the SOAR synthesizes the essential features of the block and online-based ensemble and updates the weight of each classifier, regarding its quality. It facilitates adaptive windowing to handle both gradual and sudden concept drifts. To reduce the computational cost and analyze the data stream quickly, the SOAR caches the occurred primitive patterns into a bitmap with the internal relationship. Finally, the experimental results show that the SOAR performs better classification and accuracy over data streams.

中文翻译：

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在不断变化的数据流上使用不同的集成分类器处理不同概念漂移的在线学习模型

摘要 信息技术的快速发展加速了组织产生大量高速数据流的速度。概念漂移是一个关键问题，发现数据流上的序列模式更具挑战性。集成分类器逐步学习数据，以便对概念漂移提供快速反应。集成分类器必须处理实时数据流中发生的逐渐和突然的概念漂移。因此，一种新颖的集成分类器是必不可少的，它可以对各种类型的概念漂移做出显着的反应并保持分类的准确性。这项工作提出了使用自适应在线学习规则 (SOAR) 模型动态地进行流数据挖掘，以处理逐渐和突然的模式变化并提高挖掘精度。添加分类器的数量失败，因为集成倾向于包含冗余分类器而不是高质量分类器。因此，SOAR 在集成中包含不同级别的分类器，以提供从两个概念漂移中的快速恢复。此外，SOAR 综合了块和基于在线集成的基本特征，并根据其质量更新每个分类器的权重。它有助于自适应窗口处理逐渐和突然的概念漂移。为了降低计算成本和快速分析数据流，SOAR 将发生的原始模式缓存为具有内部关系的位图。最后，实验结果表明 SOAR 对数据流具有更好的分类和准确性。