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Title: Handling latency for online learning with concept drift
Author: Marrs, Gary Russell
ISNI:       0000 0004 2752 9385
Awarding Body: University of Ulster
Current Institution: Ulster University
Date of Award: 2011
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We live in a world of ever-increasing amounts of data. There is a need to devise better and increasingly automated systems for analyzing and utilising such data, from online data streams, for the purposes of classification and prediction. Across many domains such as banking, financial markets, network management and even in biomedical monitoring of pathogen sensitivity to drugs, the competitive edge is gained by those who act on their data fastest, most accurately and keep up to date with any changes occurring in their domain. This has led to the rise of research into online learners. These automated systems serve to train themselves on received data and discover rules for use in classification and prediction. They serve to keep those rules up to date as concept drift, i.e. changing of the underlying rules, occurs. However, to date, there has been little undertaken into research as to how latency in the data stream impacts upon such learning. This thesis examines the hypothesis that latency can have a substantial impact upon the performance of online learners operating on domains with concept drift, and, that key meta-data attributes describing example passage throughout the domain may help to resolve such issues. The thesis explores what it means to be a domain by developing a generic model. The assumptions that are applied in current research upon the nature of example arrival are considered and challenged. A framework, ELISE, for simulating various latency conditions for the purposes of experimenting with meta-data attributes relating to temporal events in the example life-cycle is developed. From this several online learner algorithmic and procedural approaches are tested as a potential solution to handling latency; based upon not just isolated examples but comprehension of the temporal nature of a data stream. Finally, future work is suggested for further improvements.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available