Comparison of fusion algorithms based on logistic model of correlated classifiers

S. Y. Sohn, Y. S. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the Seventh International Conference on Information Fusion, FUSION 2004
EditorsP. Svensson, J. Schubert
Pages569-575
Number of pages7
Publication statusPublished - 2004 Nov 2
EventProceedings of the Seventh International Conference on Information Fusion, FUSION 2004 - Stockholm, Sweden
Duration: 2004 Jun 282004 Jul 1

Publication series

NameProceedings of the Seventh International Conference on Information Fusion, FUSION 2004
Volume1

Other

OtherProceedings of the Seventh International Conference on Information Fusion, FUSION 2004
CountrySweden
CityStockholm
Period04/6/2804/7/1

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Sohn, S. Y., & Kim, Y. S. (2004). Comparison of fusion algorithms based on logistic model of correlated classifiers. In P. Svensson, & J. Schubert (Eds.), Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 (pp. 569-575). (Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004; Vol. 1).