TY - GEN
T1 - Comparison of fusion algorithms based on logistic model of correlated classifiers
AU - Sohn, S. Y.
AU - Kim, Y. S.
PY - 2004
Y1 - 2004
N2 - This study compares the classification ability of various fusion algorithms (average, majority vote, median, max/min) when individual classifiers are potentially correlated. A logistic transformation of multivariate normal distribution (MVN) is used to generate the posterior probability estimates, assuring that the probability exists between 0 and 1. With varying parameters of MVN and number of classifiers, we measure the relative performance of the fusion algorithms to that of single classifier. Our results can be utilized for the selection of the most effective fusion method for given situation.
AB - This study compares the classification ability of various fusion algorithms (average, majority vote, median, max/min) when individual classifiers are potentially correlated. A logistic transformation of multivariate normal distribution (MVN) is used to generate the posterior probability estimates, assuring that the probability exists between 0 and 1. With varying parameters of MVN and number of classifiers, we measure the relative performance of the fusion algorithms to that of single classifier. Our results can be utilized for the selection of the most effective fusion method for given situation.
UR - http://www.scopus.com/inward/record.url?scp=6344248964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=6344248964&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:6344248964
SN - 917056115X
T3 - Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004
SP - 569
EP - 575
BT - Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004
A2 - Svensson, P.
A2 - Schubert, J.
T2 - Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004
Y2 - 28 June 2004 through 1 July 2004
ER -