The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online learning algorithm, which could successfully handle online anomaly detection tasks with the extremely unbalanced class distribution. As the performance evaluation, we analyze the theoretical bounds of the proposed algorithms and conduct an extensive set of experiments. The encouraging experimental results demonstrate the efficacy of the proposed algorithms over the state-of-the-art techniques on multiple data stream classification tasks.

中文翻译：

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稀疏在线学习的统一框架

在大数据时代，我们社会的数据量一直在爆炸式增长。本文旨在解决大数据流分类中的几个开放挑战。数据挖掘文献中的许多现有研究都遵循批量学习设置，该设置存在效率低和可扩展性差的问题。为了应对这些挑战，我们研究了用于大数据流分类任务的统一在线学习框架。与现有的在线数据流分类技术不同，我们提出了一个统一的稀疏在线分类（SOC）框架。基于SOC，我们推导了一种二阶在线学习算法和一种代价敏感的稀疏在线学习算法，可以成功处理类分布极不平衡的在线异常检测任务。作为绩效评价，我们分析了所提出算法的理论界限并进行了广泛的实验。令人鼓舞的实验结果证明了所提出的算法在多个数据流分类任务上优于最先进技术的有效性。