Data Mining for Rare Class Analysis

罕见事件很少发生，因此很难检测。然而，当它们发生时，其后果可能相当戏剧性，往往是负面的。示例包括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、电信电路过载和交通事故。及时准确地检测罕见事件至关重要。近年来，数据收集和存储设备的速度呈爆炸式增长。大多数组织收集有关其计算机系统中各种进程的大量数据，这些数据处于各种抽象级别，包括硬件、操作和通信系统、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，分析这些数据有可能识别前面描述的罕见事件。该项目包括调查稀有类分析中的问题，并开发一套解决这些问题的技术。该项目的重点是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鲁棒的预测模型，特别适合于稀有类问题，并使稀有类学习算法适应数据流。研究结果将在项目网站上公布（http：//www.cs.umn.edu/techniar/rare.html）。作为本研究的一部分，开发的技术将适用于广泛的应用程序，其中人们有兴趣找到那些非常重要和潜在价值非常高的少数，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.