A fully funded PhD opportunity– Ulster University, Belfast, UK: Knowledge Enhanced Imbalanced Learning (KEIL)
Omar Nibouche
A fully funded PhD opportunity– Ulster University, Belfast, UK: Knowledge Enhanced Imbalanced Learning (KEIL)
Summary: There is currently a great deal of interest in applying data analytic to real world problems characterized by imbalanced data, i.e., imbalanced learning, which are concerned across a wide range of research and application areas. For example, rare event detection, as these events occur with low frequency in daily life, but may cause far-reaching impact, including natural disaster, hazards and risks in finance and industry, and diseases. Although many methods have been proposed, there are still some key limitations. One limitation is that the learning performance is still relative low. Another limitation is the lack of an ability with most of machine learning system to explain its outputs, which has fuelled recent research in explainable AI.
This project will study knowledge-enhanced imbalanced learning, i.e., both knowledge and data are used in the process of learning, and how to structure relevant and reliable knowledge and incorporate them within the roadmap of imbalanced data analytic. The knowledge may be problem context, principles, guidelines, expert experience, or characterisation of objects. On the one hand, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully to enhance the learning performance. On the other hand, it is expected that a knowledge-enhanced learning system will have innate capabilities for explanation and interpretability.
This project provides an opportunity to combine cutting edge research at the intersection of knowledge and machine learning to address the above key challenges.
The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing goal of explainable and interpretable AI in emerging real world applications. This project will investigate fundamental research questions about knowledge-enhanced imbalanced learning and will be guided by various application scenarios where rich domain knowledge exists, such as human activity recognition, telematic data analytics, risk/safety assessment, or medical decision making.
Applicants can find further details, including shortlisting essential and desirable criteria, funding, eligibility criteria and levels of support by visiting https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1045082. For further details about the project, please contact Dr. Jun Liu (phone: +44 28 9536 5687, E-mail: j....@ulster.ac.uk).
The application deadline is Monday 7 February 2022
Recommended reading· H.X. Guo et al. (2017), Learning from class-imbalanced data: review of methods and applications, Expert Systems with Applications, DOI: 10.1016/j.eswa.2016.12.035.
· Z. Chen, et al. (2021), A hybrid data-level ensemble to enable learning from highly imbalanced dataset, Information Sciences. DOI: 10.1016/j.ins.2020.12.023.
· J. Liu, L. Martínez, A. Calzada, and H. Wang (2013), A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems. DOI: 10.1016/j.knosys.2013.08.019.
· L.H. Yang, J. Liu, Y.M. Wang, and L. Martínez (2018), A micro-extended belief rule-based system for big data multi-class classification problems, IEEE Transactions on Systems, Man, and Cybernetics: Systems. DOI: 10.1109/TSMC.2018.2872843.
· L.H. Yang, J. Liu, F.F. Ye, Y.M. Wang, C. Nugent, H. Wang, and L. Martínez (2021), Highly explainable cumulative belief rule-based system with effective rule-base modelling and inference scheme, Knowledge-Based Systems, accepted and in press.
· L.H. Yang, J. Liu, Y.M. Wang, C. Nugent, and L. Martínez (2021), Online updating extended belief rule-based system for sensor-based activity recognition expert systems with applications, Expert Systems with Applications. DOI: 10.1016/j.eswa.2021.115737.