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

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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.