Learning from imbalanced data streams differs from the traditional learning paradigm due to the issues of imbalanced classes. It has significant implications in a myriad of real-world applications, ranging from financial risk, network security, to medical diagnosis. Moreover, outliers usually appear in data streams. The issue of class imbalance or anomaly itself could negatively affect the performance of the underlying learning algorithms, and their combination makes the learning problem harder. In this work, we propose an anomaly detection aided budget online weighted learning method (BOW-LM) to identify positive and negative instances from imbalanced data streams. BOW-LM is based on the widely used Feedforward Networks with Random Weights. An agile lightweight anomaly detector is designed based on the nonlinear mapping of the network. To reduce computational complexity and to response promptly, BOW-LM employs a matrix correction technique to update the learning model by only $\mathcal {O}(L^2)$O(L2) operations for each data chunk with $L$L hidden layer nodes. Empirical studies on both synthetic and real-world datasets demonstrate the effectiveness of BOW-LM.

中文翻译：

---

不平衡数据流的异常检测辅助预算在线分类

由于不平衡类的问题，从不平衡数据流中学习不同于传统的学习范式。它在无数现实世界的应用程序中具有重要意义，从金融风险、网络安全到医疗诊断。此外，异常值通常出现在数据流中。类不平衡或异常本身的问题可能会对底层学习算法的性能产生负面影响，而它们的结合会使学习问题变得更加困难。在这项工作中，我们提出了一种异常检测辅助预算在线加权学习方法（BOW-LM），以从不平衡的数据流中识别正负实例。BOW-LM 基于广泛使用的具有随机权重的前馈网络。基于网络的非线性映射设计了一种敏捷的轻量级异常检测器。为了降低计算复杂度并快速响应，BOW-LM 采用了矩阵校正技术来更新学习模型，只需对每个数据块进行 $\mathcal {O}(L^2)$O(L2) 操作，其中 $L$L隐藏层节点。对合成数据集和真实数据集的实证研究证明了 BOW-LM 的有效性。