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Learning concept evolution in data streams [Self-Funded Students Only]

   Cardiff School of Computer Science & Informatics

  Dr Yuhua Li  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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·        outdated concepts retiring.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 

In order to be considered candidates must submit the following information: 

  • Supporting statement 
  • CV 
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
  • Qualification certificates and Transcripts
  • Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
  • References x 2 
  • Proof of English language (if applicable)

Contact Yuhua Li () for information on the project.

If you have any questions or need more information, please contact 

Funding Notes

This project is offered for self-funded students only, or those with their own sponsorship or scholarship award.


J.Lu et al. (2019) “Learning under Concept Drift: A Review,” IEEE T KDE.,
Y Li, L Maguire (2011) “Selecting critical patterns based on local geometrical and statistical information,” IEEE T PAMI.

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