TY - GEN

T1 - The set classification problem and solution methods

AU - Ning, Xia

AU - Karypis, George

PY - 2009/12/31

Y1 - 2009/12/31

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=72749128301&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=72749128301&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:72749128301

SN - 9781615671090

T3 - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

SP - 843

EP - 854

BT - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133

T2 - 9th SIAM International Conference on Data Mining 2009, SDM 2009

Y2 - 30 April 2009 through 2 May 2009

ER -