The project is in the area of machine learning for text classification and more specifically case base maintenance.
Because case-based classification techniques are local learners they are particularly susceptible to the problem of noisy training data.
Previous work by project leaders has shown that in certain domains, particularly spam filtering, standard case-base editing techniques are unsuitable.
This project aims to investigate why the newly developed blame-based noise reduction technique is particularly successful in the spam domain and whether its success can be transferred to the broader, but related, domains of text classification and fraud detection.
Furthermore, the project will investigate whether the techniques of feature free learning and active learning can be applied to the noise reduction problem.
Applicants for this studentship are expected to have a good honours degree in computer science or a related discipline, excellent technical and programming skills and strong written and spoken English.
Previous experience of artificial intelligence or machine learning is desirable but not essential.