BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Decision making in hierarchical multi-label classification (HMC) p
 roblems - Haiyan Huang (UC Berkeley)
DTSTART:20160708T150000Z
DTEND:20160708T160000Z
UID:TALK64760@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:Multi-label classification assigns an object to multiple class
 es. Very often the classes can be organized in the form of a tree or direc
 ted acyclic graph (DAG)\, and the class assignments are required to respec
 t the hierarchy: an object can be assigned to a class only if it has been 
 assigned to the class’s parent in the hierarchy. \n\nMulti- label classi
 fication with this hierarchical constraint is known as hierarchical multi-
 label classification (HMC). HMC has been increasingly common in modern app
 lications such as disease diagnosis (i.e.\, disease ontology takes disease
  concepts organized into a DAG).\n\nIn this talk\, we will mainly discuss 
 how to make “optimal” decisions in HMC given classifiers of individual
  classes. In particular\, we introduce a new procedure\, based on transfor
 ming the individual classifier scores into local precision rates or local 
 false discovery rates\, to make class assignments along either a tree- or 
 DAG-structured hierarchy. This method will lead to an optimal hit curve un
 der some reasonable conditions.  This work was motivated from a project on
  computational disease diagnosis we did a few year ago. \n\nThis is a join
 t work with Christine Ho (a Stat PhD student at UCB)\, Wayne Lee (Quantita
 tive researcher at The Climate Corporation) and Dr. Ci-ren Jiang (Research
 er fellow at Academia Sinica\, Taiwan).\n\nProf. Haiyan Huang Bio\n\nHaiya
 n Huang is currently an Associate Professor in the Department of Statistic
 s at UC Berkeley. Meanwhile\, she is affiliated with the graduate group in
  Biostatistics and the center for Computational Biology on campus. Prior t
 o joining the faculty member of UC Berkeley\, Haiyan Huang did a postdoc i
 n Applied Statistics and Computational Biology at Harvard University. She 
 obtained her Ph.D. in Applied Mathematics from the University of Southern 
 California and received a B.S. in Mathematics from Peking University\, Chi
 na. As an applied statistician\, her research is at the interface between 
 statistics and data-rich scientific disciplines such as biology. Over the 
 past few decades\, rapidly evolving biological technologies have generated
  enormous high-dimensional\, complex\, noisy data\, presenting increasingl
 y pressing challenges to statistical and computational science. Her group 
 has devoted to addressing various modeling and analysis challenges from th
 ese data.
LOCATION:CRUK CI Room 009/009A
END:VEVENT
END:VCALENDAR