Extracting knowledge from data streams requires fast incremental algorithms that are able to handle unlimited processing and ever-changing data with finite memory. A strategy for this challenge is the use of ensembles owing to their ability to tackle concept drift and achieve highly accurate predictions. However, ensembles often require a lot of computational resources. In this study, we propose a novel ensemble-based classification algorithm for data streams, Adaptive Regularized Ensemble (ARE), with low demand for computational resources. The algorithm combines strategies that contribute to high prediction accuracy using only incorrectly classified instances into the training step, random-sized feature subspace for each ensemble element and classifier selection for final ensemble voting. After an extensive experimental study, ARE exhibited high predictive performance and outperformed state-of-the-art ensembles on data streams for real and synthetic datasets while requiring a low processing time and memory consumption.

中文翻译：

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用于演化数据流分类的自适应正则化集成

从数据流中提取知识需要快速增量算法，这些算法能够使用有限的内存处理无限的处理和不断变化的数据。应对这一挑战的策略是使用集成，因为它们能够解决概念漂移并实现高度准确的预测。然而，集成通常需要大量的计算资源。在本研究中，我们提出了一种新颖的基于集成的数据流分类算法，自适应正则化集成（ARE），对计算资源的需求较低。该算法将有助于高预测精度的策略相结合，仅在训练步骤中使用错误分类的实例、每个集成元素的随机大小的特征子空间以及最终集成投票的分类器选择。经过广泛的实验研究，ARE 在真实和合成数据集的数据流上表现出较高的预测性能，并且优于最先进的集成，同时需要较低的处理时间和内存消耗。