III: Small: Integrating Casual Discovery and Feature Selection with Streaming Features

III：小：将休闲发现和特征选择与流媒体功能相结合

• 批准号: 1613950
• 负责人: Xindong Wu
• 金额: $ 49.79万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613950&HistoricalAwards=false
• 关键词: III; Small; Integrating; Casual; Discovery

With the advent of emerging massive datasets in image processing,biology, finance, and so on, traditional data mining systemsface new challenges to induce knowledge and discover causalrelations in dynamic streaming feature environments, where newfeatures continuously stream in over time. These challenges include(1) continuous growth of feature volumes over time, (2) a huge featurespace, even of unknown or infinite size, and (3) not all featuresbeing available before learning begins. These challenges call for anew learning paradigm with continuously increasing features. In thisproject, we take the increasing feature volumes as streaming features,and the corresponding learning problem is referred to as OnlineLearning with Streaming Features (OLSF). Since existing onlinelearning efforts mostly deal with data with increasing observationsbut fixed feature dimensions, OLSF provides a unique chance to unfoldand characterize pattern trends for dynamic systems with streamingfeatures.This project aims to address two fundamental issues for OLSF: (1)causal discovery with sequentially increasing feature dimensions; and(2) causal relations for feature selection. We design novel methodsand algorithms for causal discovery in OLSF and establish formal connectionsbetween casual discovery and feature selection by investigating themutual benefits between them in the context of online stream featurelearning. To evaluate the proposed research, we conduct empiricalstudies on a large body of benchmark datasets, as well as with adomain-specific real-world case study in personalized news filteringand summarization where the feature space changes over time. Thenew algorithms and techniques in this project will advance our abilityto discover knowledge from dynamic systems using streaming featureswith bounded resources. The spectrum of the methods from the projectwill not only enrich our knowledge and understanding of patterndiscovery and machine learning for dynamic systems, but also provide anew view to capture and characterize dynamic systems from a streamingfeature perspective.

随着图像处理、生物学、金融等领域新兴海量数据集的出现，传统数据挖掘系统面临着在动态流特征环境中引入知识和发现因果关系的新挑战，其中新特征随着时间的推移不断涌入。这些挑战包括（1）特征量随着时间的推移不断增长，（2）巨大的特征空间，甚至未知或无限大小，以及（3）在学习开始之前并非所有特征都可用。这些挑战需要具有不断增加功能的新学习范式。在这个项目中，我们将不断增加的特征量作为流特征，相应的学习问题被称为带有流特征的在线学习（OLSF）。由于现有的在线学习工作主要处理具有增加的观测值但特征维度固定的数据，OLSF 提供了一个独特的机会来展开和表征具有流特征的动态系统的模式趋势。该项目旨在解决 OLSF 的两个基本问题：（1）顺序增加的因果发现特征尺寸； (2)特征选择的因果关系。我们为 OLSF 中的因果发现设计了新颖的方法和算法，并通过研究在线流特征学习背景下偶然发现和特征选择之间的互惠关系，在偶然发现和特征选择之间建立了正式的联系。 为了评估所提出的研究，我们对大量基准数据集进行了实证研究，并对个性化新闻过滤和摘要中特定领域的现实世界案例研究进行了研究，其中特征空间随时间变化。 该项目中的新算法和技术将提高我们使用有限资源的流功能从动态系统中发现知识的能力。该项目的方法范围不仅将丰富我们对动态系统的模式发现和机器学习的知识和理解，而且还提供了从流特征角度捕获和表征动态系统的新视角。