论文标题
通过使用基于注意的隔离林改善了异常检测
Improved Anomaly Detection by Using the Attention-Based Isolation Forest
论文作者
论文摘要
提出了一种称为基于注意的隔离森林（Abiforest）的新修饰，以解决异常检测问题。它以纳达拉亚·沃特森（Nadaraya-Watson）回归的形式将注意力机制纳入了隔离林，以改善解决异常检测问题的解决方案。修改基础的主要思想是，根据实例和树木本身，将注意力重量分配给树木的每个路径。建议将Huber的污染模型用于定义注意力重量及其参数。结果，注意力的权重是线性取决于可学习的注意参数，这些参数是通过解决标准线性或二次优化问题来训练的。可观的探索可以看作是隔离林的第一个修饰，它以简单的方式包含了注意力机制，而无需应用基于梯度的算法。合成数据集和真实数据集的数值实验说明了优于abiforest的结果。提出的算法代码可用。
A new modification of Isolation Forest called Attention-Based Isolation Forest (ABIForest) for solving the anomaly detection problem is proposed. It incorporates the attention mechanism in the form of the Nadaraya-Watson regression into the Isolation Forest for improving solution of the anomaly detection problem. The main idea underlying the modification is to assign attention weights to each path of trees with learnable parameters depending on instances and trees themselves. The Huber's contamination model is proposed to be used for defining the attention weights and their parameters. As a result, the attention weights are linearly depend on the learnable attention parameters which are trained by solving the standard linear or quadratic optimization problem. ABIForest can be viewed as the first modification of Isolation Forest, which incorporates the attention mechanism in a simple way without applying gradient-based algorithms. Numerical experiments with synthetic and real datasets illustrate outperforming results of ABIForest. The code of proposed algorithms is available.
