AIS: Learning from Initially Labeled Nonstationary Streaming Data

AIS：从最初标记的非平稳流数据中学习

• 批准号: 1310496
• 负责人: Robi Polikar
• 金额: $ 29.75万
• 依托单位: Rowan University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1310496&HistoricalAwards=false
• 关键词: AIS; Learning; Initially; Labeled; Nonstationary

The objective of this research is to develop a novel framework for analysis of large volumes of streaming data, where data characteristics change over time, and all new data are unlabeled and unstructured. The proposed work will be seminal for analyzing streaming nonstationary and unlabeled data while removing the unrealistic assumptions and simplifications made by existing approaches. The proposed approach uses small initial training data to label the currently unlabeled new data, creates an envelope around this data and shrinks the envelope to determine the core support region of the data. Samples are extracted from this region to serve as future training data to iteratively label the new incoming unlabeled drifting data. Once initialized, this approach never needs labeled data, and can indefinitely track the changes in data distribution. The primary intellectual merit is a new framework, addressing arguably one of the most challenging learning problems, accomplished by strategic integration of machine learning and computational geome-try. Significant fundamental knowledge will be obtained through formal development of this framework, which will then benefit many real-world applications, currently not properly addressed under existing ap-proaches. Broader Impacts: The proposed framework promises to bring us closer to truly adaptive and intelligent (brain-like) learning, and allow proper analysis of data drawn from aforementioned scenarios, whose ap-plications include network intrusion, cyber security, web-usage analysis, natural language processing, anomaly detection, climate change and energy demand analysis. Project's educational component will form Integrated Research and Learning Communities, drawing undergraduate students to whom this field has been mostly inaccessible.

本研究的目标是开发一种新的框架，用于分析大量的流数据，其中数据特征随时间而变化，并且所有新数据都是未标记和非结构化的。所提出的工作将是开创性的分析流非平稳和未标记的数据，同时消除现有方法所做的不切实际的假设和简化。该方法使用较小的初始训练数据来标记当前未标记的新数据，围绕该数据创建包络并收缩包络以确定数据的核心支持区域。从该区域提取样本以用作未来的训练数据，以迭代地标记新的传入的未标记的漂移数据。一旦初始化，这种方法就不需要标记数据，并且可以无限期地跟踪数据分布的变化。主要的智力价值是一个新的框架，解决了可以说是最具挑战性的学习问题之一，通过机器学习和计算几何学的战略整合来实现。通过这个框架的正式开发，将获得重要的基础知识，这将有利于许多现实世界的应用程序，目前没有适当地解决现有的ap-proaches。更广泛的影响：拟议的框架有望使我们更接近真正的自适应和智能（类脑）学习，并允许从上述场景中提取的数据进行适当的分析，其应用包括网络入侵，网络安全，网络使用分析，自然语言处理，异常检测，气候变化和能源需求分析。该项目的教育部分将形成综合研究和学习社区，吸引本科生，他们大多无法进入这一领域。