workshop.bib

@inproceedings{Ning2008,
  author = {\textbf{X. Ning} and George Karypis},
  title = {The Set Classification Problem and Solution Methods},
  booktitle = {IEEE International Conference on Data Mining Workshops},
  year = {2008},
  pages = {720-729},
  abstract = {This paper focuses on developing classification algorithms for problems
	in which there is a need to predict the class based on multiple observations
	(examples) of the same phenomenon (class). These problems give rise
	to a new classification problem, referred to as set classification,
	that requires the prediction of a set of instances given the prior
	knowledge that all the instances of the set belong to the same unknown
	class. This problem falls under the general class of problems whose
	instances have class label dependencies. Four methods for solving
	the set classification problem are developed and studied. The first
	is based on a straightforward extension of the traditional classification
	paradigm whereas the other three are designed to explicitly take
	into account the known dependencies among the instances of the unlabeled
	set during learning or classification. A comprehensive experimental
	evaluation of the various methods and their underlying parameters
	shows that some of them lead to significant gains in performance.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://dx.doi.org/10.1109/ICDMW.2008.113},
  file = {papers/Ning2008.pdf}
}