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In applications with concept evolution, new concepts emerge in data stream and existing/known concepts disappear over time, e.g., new types of attack in a computer network and new topics of interest in social medial data stream. This project aims to develop novel methods for tackling the challenging issue of concept evolution to enable the learned models to accommodate new concepts. It will achieve the following objectives:
· known concepts modelling;
· novel instances detection and accumulation;
· new concepts detection and integration;
· outdated concepts retiring.
In order to achieve single pass real-time analytics for streaming data presenting evolving concepts, it will focus on techniques that can be efficiently implemented in parallel. In particular, instance-based and tree-based modelling techniques will be investigated to develop efficient and effective algorithms for the achievement of the objectives.
Keywords: concept drift, instance selection, novelty detection, model adaptation, data stream.
Academic Criteria - A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.
Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
How to apply:
Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below
This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics
In order to be considered candidates must submit the following information:
Contact Yuhua Li ([Email Address Removed]) for information on the project.
If you have any questions or need more information, please contact [Email Address Removed]
Research output data provided by the Research Excellence Framework (REF)
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