Postgraduate research

Deep learning for imbalanced classification

Qualification: PhD

Department: School of Computing and Mathematical Sciences

Application deadline: 31 July 2022

Start date: 9 January 2023

Overview

Supervisor:

Project description:

Standard predictive models have been designed with the assumption of an equal number of samples over all the classes. Imbalanced classification poses a major challenge to the community of artificial intelligence, where the distribution of samples for different classes is biased. In such case, the classification outcome is dominated by the biased distribution, where there are a small amount of samples in the minority class and a large number of samples in the majority class. Skewed or biased data distributions may arise in vast applications such as disease diagnosis, cyber security, image recognition and earth observation. 

Imbalanced classification problems have been explored for many years in the community of machine learning. Methods for addressing these problems can be categorised as data- and algorithm-level and hybrid approaches. Data-level methods modify the training distributions by over- and under-sampling [1]. Unlike data sampling, algorithm-level approaches target enhancing the learning and decision process to increase the importance of the positive class [2]. Data- and algorithm-level methods can be combined to deal with sampling and cost-sensitive learning [3]. 

Among those available technologies, deep learning has achieved promising successes due to its high learning capacity [4]. Despite its powerful capabilities, deep learning architectures are still vulnerable to imbalanced data distributions such as complex data representations [5].

In this project, we intend to explore the possibility of incorporating boosting concepts in deep learning architectures for imbalanced classification. We wish to achieve the following objectives throughout the entire project:

  1. To develop a novel deep learning framework with boosting for imbalanced classification.
  2. To develop a new representation learning strategy to deal with multi-task applications with scarce samples and labels.
  3. To comprehensively evaluate the systems proposed in (1) and (2).

The expected outcome of the proposed project include one fully working demo system, 2 papers submitted to top journals, e.g. Journal of Machine Learning Research, and 2 papers submitted to top conferences such as ICML.

References: 

  1. J. Van Hulse, T.M. Khoshgoftaar, A. Napolitano. Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th international conference on machine learning. ICML ’07. ACM, New York, NY, USA. 2007. p. 935–42.
  2. C.X. Ling, V.S. Sheng. In: Sammut C, editor. Cost-sensitive learning and the class imbalanced problem. 2007.
  3. H. He, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263–1284.
  4. F. Bao, Y. Deng, Y. Kong, Z. Ren, J. Suo, and Q. Dai, “Learning deep landmarks for imbalanced classification,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 8, pp. 2691–2704, 2020.
  5. X. Jing, X. Zhang, X. Zhu, F. Wu, X. You, Y. Gao, S. Shan, and J. Yang, “Multiset feature learning for highly imbalanced data classification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 1, pp. 139–156, 2021.

Funding

Funding

This 3.5 year PhD studentship provides:

  • Annual Stipend at UKRI rates (£16,062 for 2022/23)
  • Tuition fee waiver
  • RTSG

Entry requirements

Entry requirements

Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better in a relevant subject or overseas equivalent.

The University of Leicester English language requirements apply where applicable.

Informal enquiries

Informal enquiries

Project / Funding Enquiries: CMSPGR@leicester.ac.uk 

Application enquiries: pgradmissions@le.ac.uk 

How to apply

How to apply

To apply please use the Apply button at the bottom of the page and select January 2023 from the dropdown menu.

With your application, please include:

  • CV
  • Personal statement explaining your interest in the project, your experience and why we should consider you
  • Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken
  • Evidence of English language proficiency if applicable
  • In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.

In the funding section, please specify CSE Zhou in the space provided

In the proposal section please provide the name of the supervisors and project title (you do not need a research proposal)

Eligibility

Eligibility

Open to UK and International applicants.

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