CSE Fac Candidate, Jingrui He

Imbalanced data sets are prevalent in many real applications. It is often
the case that people are only interested in the minority classes. The
focus of my thesis is rare category analysis, which refers to the problem
of detecting and characterizing the minority classes in an unlabeled,
imbalanced data set. In this talk, I will introduce different aspects of
rare category analysis, including rare category detection for detecting
examples from new minority classes, rare category characterization for
identifying examples from known minority classes, co-selection of relevant
features and examples from the minority classes, etc. Along with
theoretical analysis, I will also present experimental results showing the
effectiveness of the proposed algorithms.
Jingrui He is a Ph.D candidate in Machine Learning Department at
Carnegie Mellon University. She holds an M.S. degree and a B.S. degree
from Tsinghua University, P.R. China. Her research interests include
statistical learning for rare category analysis, active learning,
multimedia, and spam filtering.