TITLE: The Many Faces of ROC Analysis in Machine Learning
PRESENTER: Peter Flach
AFFILIATION: University of Bristol, UK
DATE: Friday 27th June 2003
TIME: 2:00pm to 3.00pm
PLACE: CSE K17 1st Floor Seminar Room
Receiver Operating Characteristics (ROC) analysis has been introduced relatively recently in machine learning. The key idea is to distinguish performance on the positive and negative class, which allows us to select an optimal classifier even if the class or misclassification cost distribution varies from training to application context. However, ROC analysis has a much wider applicability than model selection. In this talk I will present some recent work on applying ROC analysis in decision tree and naive Bayes model building. In addition, I will outline a general framework for understanding machine learning metrics through the use of ROC isometric plots.
BIOGRAPHY OF SPEAKER:
Peter A. Flach, Reader in Machine Learning, Dept. of Computer Science, Univ. of Bristol, www.cs.bris.ac.uk/~flach/ is currently on study leave at NICTA and UNSW, School of Comp Sci and Eng, Univ of New South Wales, Sydney, Australia 2052
School of Computer Science & Engineering, UNSW.