Learning in the presence of change: challenges and algorithms for data stream mining

传感器网络产生的数据流、客户点击流、科学数据在我们的社会中无处不在，从这些快速发展的知识库中提取知识是我们今天面临的最重大挑战之一。所提出的研究的主要目标是开发新的算法，在这种非平稳环境中学习。具体来说，拟议的工作福尔斯自适应机器学习领域，重点是开发主动的增量算法，用于在快速发展的数据流中挖掘多个概念。这项工作的应用领域很多，与物联网（IoT）有关，包括智能城市规划中的个性化路由，计算机网络中的入侵检测，金融机构和保险业的欺诈检测以及社交媒体分析。我们的目标是设计资源感知算法，构建准确，可靠的模型，无缝地适应数据流的潮起潮落。近年来的研究集中在能够从不断变化的数据流中学习的自适应算法的开发上。通常，这些算法一次学习一个实例，并以这种方式检测和适应变化。然而，仍有一些重要的研究问题没有得到解答。需要新的解决方案来从不完整的数据中学习，并处理数据分布中的多个并发异构更改。缺乏识别当前相关特征子集的方法。例如，在欺诈检测应用中，需要技术来检测人的年龄何时不再是相关指标。目前还不清楚如何针对不经常出现的概念建立准确的模型。这种类别不平衡问题仍然是一个公开的挑战，特别是在工程系统中的故障检测等环境中。从动态演变的存储库中进行机器学习仍然给我们的研究社区带来了许多艰巨的挑战。为了满足这一需求，本研究的核心将集中在五个相互关联的研究问题，涉及a）数据到达的速度和数量，B）处理数据分布中的异构变化，c）新兴概念和特征的检测，d）在存在高度偏斜的数据分布的情况下进行学习，以及e）不完美和不完整数据的建模。

Data streams as produced by sensor networks, customer click streams, scientific data are ubiquitous in our society, and extracting knowledge from these fast-evolving repositories is one of the most significant challenges that we face today. The principal objective of the proposed research is to develop novel algorithms for learning in such non-stationary environments. Specifically, the proposed work falls within the area of adaptive machine learning and focuses on the development of pro-active, incremental algorithms for mining multiple concepts in fast-evolving data streams. Application areas of this work are numerous and are related to the Internet of Things (IoT), including personalized routing in urban planning for smarter cities, intrusion detection in computer networking, fraud detection in financial institutions and the insurance industry, and social media analysis. Our aim is to design resource-aware algorithms that construct accurate, reliable models that adapt seamlessly to the ebb and flow of data streams. In recent years research has focused on the development of adaptive algorithms capable of learning from evolving data streams. Typically, these algorithms learn one instance at a time and in that manner can detect, and adapt to, changes. However, there are vital research questions that remain unanswered. New solutions are needed to learn from incomplete data and to handle multiple, concurrent heterogeneous changes in data distributions. Methods that recognise the current relevant subset of features are lacking. In a fraud-detection application, for instance, techniques are needed to detect when a person's age ceases to be a relevant indicator. It is still not clear how to build accurate models against infrequently appearing concepts. This class imbalance problem remains an open challenge, especially in environments such as fault detection in engineering systems. Machine learning from dynamically evolving repositories still presents our research community with many daunting challenges. To address this demand, the core of this proposed research will focus on five interrelated research questions, related to a) the velocity and volume of data arrival, b) the handling of heterogeneous changes in data distributions, c) the detection of emerging concepts and features, d) learning in the presence of highly skewed data distributions, and e) the modeling of imperfect and incomplete data.