Scalable Data Mining by Meta-Learning

该项目开发了将数据挖掘任务扩展到大型分布式数据库的方法。元学习通过将学习程序并行应用于数据库分区来计算基本分类器，然后通过另一个学习过程将它们集成，输出一个“元分类器”。元学习基本分类器的相关性提高了整体预测的准确性。通过并行处理和减少每个学习过程的串行时间来提高效率。元学习可以通过数据简化和分层组织中的并行处理进行扩展。我们探索、分析和验证了许多元学习的方法，并提供了可演示的系统，允许其他研究人员直接利用我们的结果。我们通过在互联网上启动“学习代理”来开发结果技术的现场测试，并通过“元学习代理”将他们的集体知识结合起来。许多国防和商业应用将受益于通过集成和分析大量广泛分布的数据来学习新知识。在这个项目上进行协作的一个组织是金融服务技术联盟。FSTC已经定义了一个适用于元学习技术的现实应用，并提供了真实的数据集和学习任务。项目参与者将展示建议的方法在互联网电子商务应用中的电子交易欺诈检测的有效性。

This project develops methods to scale data mining tasks to large, inherently distributed databases. Meta-learning computes base classifiers by applying learning programs in parallel to database partitions, and then integrates them by another learning process outputting a `meta-classifier`. Meta-learning the correlations of the base classifiers boosts overall predictive accuracy. Efficiency is achieved by parallel processing and reduced serial time per learning process. Meta-learning is scalable by data reduction and parallel processing in hierarchical organizations. We explore, analyze and validate a number of approaches to meta-learning, and deliver demonstrable systems allowing other researchers to directly utilize our results. We develop field tests of the resultant technology by launching `learning agents` over the internet and combine their collective knowledge by `meta-learning agents`. Many defense and commercial applications will benefit from learning new knowledge by integrating and analyzing very large amounts of widely distributed data. One organization working collaboratively on this project is the Financial Services Technology Consortium. The FSTC has defined a realistic application amenable to meta-learning techniques and are providing real datasets and learning tasks. The project participants will demonstrate the efficacy of the proposed approach for fraud detection of electronic transactions in electronic commerce applications of the internet.