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

随着图像处理、生物学、金融等领域海量数据集的出现，传统的数据挖掘系统面临着在动态流特征环境中归纳知识和发现因果关系的新挑战。这些挑战包括（1）随着时间的推移，特征量不断增长，（2）巨大的特征空间，甚至未知或无限大，以及（3）在学习开始之前，并非所有特征都可用。这些挑战要求新的学习范式与不断增加的功能。在这个项目中，我们把不断增长的特征量作为流特征，相应的学习问题称为流特征在线学习（OLSF）。由于现有的在线学习工作主要是处理增加的观察数据，但固定的特征尺寸，OLSF提供了一个独特的机会来展开和表征模式的趋势与streamingfeatures动态系统。本项目旨在解决两个基本问题OLSF：（1）因果发现与顺序增加的特征尺寸;（2）因果关系的特征选择。我们设计了新的方法和算法，在OLSF因果发现和特征选择之间建立正式的连接，通过调查他们之间的互利在线流特征学习的背景下。 为了评估所提出的研究，我们进行了大量的基准数据集，以及与adomain特定的真实世界的案例研究个性化的新闻filteringand摘要的特征空间随着时间的推移而变化的psychicalstudies。 这个项目中的新算法和技术将提高我们使用有限资源的流特征从动态系统中发现知识的能力。该项目的方法不仅丰富了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。

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.