Roveri Manuel
IEEE Trans Neural Netw Learn Syst. 2019 Sep;30(9):2570-2582. doi: 10.1109/TNNLS.2018.2886956. Epub 2019 Jan 18.
Learning under concept drift is a novel and promising research area aiming at designing learning algorithms able to deal with nonstationary data-generating processes. In this research field, most of the literature focuses on learning nonstationary probabilistic frameworks, while some extensions about learning graphs and signals under concept drift exist. For the first time in the literature, this paper addresses the problem of learning discrete-time Markov chains (DTMCs) under concept drift. More specifically, following a hybrid active/passive approach, this paper introduces both a family of change-detection mechanisms (CDMs), differing in the required assumptions and performance, for detecting changes in DTMCs and an adaptive learning algorithm able to deal with DTMCs under concept drift. The effectiveness of both the proposed CDMs and the adaptive learning algorithm has been extensively tested on synthetically generated experiments and real data sets.
在概念漂移下进行学习是一个新颖且有前景的研究领域，旨在设计能够处理非平稳数据生成过程的学习算法。在这个研究领域中，大部分文献聚焦于学习非平稳概率框架，同时也存在一些关于在概念漂移下学习图和信号的扩展研究。本文首次探讨了在概念漂移下学习离散时间马尔可夫链（DTMC）的问题。更具体地说，本文采用一种主动/被动相结合的方法，引入了一类变化检测机制（CDM），这些机制在所需假设和性能方面存在差异，用于检测DTMC中的变化，以及一种能够处理概念漂移下的DTMC的自适应学习算法。所提出的CDM和自适应学习算法的有效性已在合成生成的实验和真实数据集上进行了广泛测试。
