Incremental machine learning with emerging new classes and application to edge computing [Self-Funded Students only]

Standard classification methods can only classify pre-defined classes, i.e., they classify a new instance into one (or multiple) of the known classes. For example, for building a classifier for viral respiratory diseases, we need to train the classification model on a dataset with pre-defined classes such as MERS and SARS. At the time of developing a model for disease classification, the classifier is trained on available data which contains only, e.g., MERS and SARS. Such a classifier can only classify MERS and SARS diseases, it will be unable to deal with the emergence of new diseases such as COVID-19 in the future. In order to deal with the emergence of new classes, a novel approach is needed to learn a classifier that is able to detect newly emerging classes and adapt the classifier accordingly. Such a classifier learning paradigm with new classes has numerous applications, e.g., self-driving cars manoeuvring in novel traffic scenarios, malware detector dealing with new type of network attacks, robotic soldiers navigating in new type of terrains, etc.

This project aims to develop a novel approach to learning a classifier that is capable to classifier emerging and novel classes. The proposed approach will address two main challenges: effective detection of emerging classes and just-in-time adaptation of classifiers for new classes. Emerging class detection will be built on the latest advances of novelty detection (novelty detection is a machine learning technique that learns a model based on only known classes to detect instances coming from a novel class), just-in-time adaptation will develop a novel incremental learning strategy to integrate new classes into current classifier. The developed algorithms will be evaluated on a use case in edge computing or Internet of Things (e.g., new type of network attacks).

It is expected to obtain the following deliverables throughout the project:

• Algorithm for effective detection of emerging classes
• Algorithm for adopting emerging classes into classifier
• High quality journal and conference publications

Contact Yuhua Li ([Email Address Removed]) for information on the project.

Keywords: incremental learning, novelty detection, model adaptation, data stream classification, deep learning.

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: 

• 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) 

Interview - If the application meets the entrance requirements, you will be invited to an interview

If you have any additional questions or need more information, please contact [Email Address Removed]