Data Mining for Rare Class Analysis

罕见事件发生的频率非常低，因此很难检测到。然而，当它们确实发生时，它们的后果往往是相当戏剧性的，而且往往是负面的。例如，网络入侵和安全漏洞、心脏事件、信用卡和其他类型的金融欺诈、电信线路超载和交通事故。对罕见事件的及时和准确检测至关重要。近年来，数据采集和存储设备的速度出现了爆炸性增长。大多数组织在不同抽象级别收集有关其计算机系统中各种进程的大量数据，包括硬件、操作系统和通信系统、数据库查询日志等。这些全面的事件日志提供了丰富的数据，对这些数据的分析有可能识别前面描述的罕见事件。该项目包括研究稀有类分析中的问题，并开发一套解决这些问题的技术。本项目的重点是用于稀有类分析的监督学习方法。具体任务包括开发新的特征选择方案和稳健的预测模型，特别适用于稀有类问题，以及使稀有类学习算法适应数据流。研究结果将在项目网站(http://www.cs.umn.edu/~kumar/rare.html).)上公布作为这项研究的一部分，开发的技术将适用于广泛的应用程序，在这些应用程序中，人们有兴趣找到那些具有高度重要性和潜在非常高价值的少数、不寻常的特殊情况。

Rare events occur very infrequently and are thus very difficult to detect. However, when they do occur, their consequences can be quite dramatic quite often in a negative sense. Examples include network intrusions and security breaches, cardiac events, credit card and other types of financial fraud, telecom circuit overloads, and traffic accidents. Timely and accurate detection of rare events is critical. Recent years have seen an explosive growth in the speed of data collection and storage devices. Most organizations collect quantities of data about various processes in their computer systems, at various levels of abstraction, including hardware, operating and communication systems, database query logs, etc. These comprehensive event logs provide a wealth of data, analysis of which has the potential to identify the rare events described earlier. The project includes investigation of the issues in rare class analysis, and development of a suite of techniques to address them. The focus of this project is on supervised learning methods for rare class analysis. Specific tasks include development of novel feature selection schemes and robust predictive models especially suited for rare class problems, and adapting rare class learning algorithms to data streams. The research results will be made publicly available at the project website (http://www.cs.umn.edu/~kumar/rare.html). The techniques developed, as part of this research will be applicable across a wide spectrum of applications in which one is interested in finding those few, unusual, special cases which are highly significant and of potentially very high value.