Welcome to the homepage of our research group: Vision and Learning at Stellenbosch (VLS). Here you can find information about members, research, workshops and upcoming as well as previous talks.
Meetings are held on Fridays at 13:00 in the Computer Science seminar room (5th floor, above Applied Maths), with coffee afterwards.
Anyone is welcome to attend!
Fault diagnosis is an important component of process monitoring, relevant in the greater context of developing safer, cleaner and more cost efficient industrial processes. Data-driven / feature extraction approaches to fault diagnosis exploit the many measurements available on modern plants. Certain current feature extraction approaches are hampered by their linearity assumptions, motivating the investigation of nonlinear methods. This work looks at using random forests in fault diagnostic frameworks.
Random forests are recently proposed statistical learning tools, deriving their predictive accuracy from the nonlinear nature of their constituent decision tree members and the power of ensembles. Random forest committees provide more than just predictions; model information on data proximities can be exploited to provide random forest features. Variable importance measures show which variables are closely associated with a chosen response variable, while partial dependencies indicate the relation of important variables to said response variable.
Previous talks abstracts are in the archive.