论文标题
使用kullback-leibler差异跟踪更改以进行持续学习
Tracking changes using Kullback-Leibler divergence for the continual learning
论文作者
论文摘要
最近，持续学习引起了很多关注。重要的问题之一是\ emph {概念漂移}的出现，它由改变传入数据的概率特征组成。在分类任务的情况下，这种现象破坏了模型的性能，并对所达到的预测质量产生负面影响。大多数当前方法对原始数据应用统计学习和相似性分析。但是，流媒体数据中的相似性分析仍然是一个复杂的问题，这是由于时间限制，非专业值，快速决策速度，可扩展性等。本文介绍了一种新方法，用于监视多维数据流的概率分布的变化。为了衡量变化的速度，我们分析了流行的kullback-leibler差异。在实验研究中，我们展示了如何使用此指标来预测概念漂移的发生并理解其性质。获得的结果鼓励对所提出的方法及其在实际任务中的应用进一步工作，在这种实际任务中，对概念的未来出现的预测起着至关重要的作用，例如预测性维护。
Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model's performance and negatively affects the achieved prediction quality. Most current methods apply statistical learning and similarity analysis over the raw data. However, similarity analysis in streaming data remains a complex problem due to time limitation, non-precise values, fast decision speed, scalability, etc. This article introduces a novel method for monitoring changes in the probabilistic distribution of multi-dimensional data streams. As a measure of the rapidity of changes, we analyze the popular Kullback-Leibler divergence. During the experimental study, we show how to use this metric to predict the concept drift occurrence and understand its nature. The obtained results encourage further work on the proposed methods and its application in the real tasks where the prediction of the future appearance of concept drift plays a crucial role, such as predictive maintenance.
