Combining classifiers for multisensor data fusion

Devi Parikh, Min T. Kim, Joseph Oagaro, Shreekanth Mandayam, Robi Polikar

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

8 Scopus citations

Abstract

Learn++ was recently introduced as an ensemble of classifiers based incremental learning algorithm, capable of retaining formerly acquired knowledge while learning novel information content from new datasets without requiring access to any of the previously seen data. In this contribution, we discuss the conceptual similarity between incremental learning and data fusion, the latter also requiring learning from new data, albeit composed of a different set of features. Following the technical description of the algorithm, we present our recent promising results on a realworld data fusion application of non-destructive evaluation for pipeline defect identification.

Original languageEnglish (US)
Title of host publication2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages1232-1237
Number of pages6
DOIs
Publication statusPublished - Dec 1 2004
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: Oct 10 2004Oct 13 2004

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2
ISSN (Print)1062-922X

Other

Other2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
CountryNetherlands
CityThe Hague
Period10/10/0410/13/04

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Parikh, D., Kim, M. T., Oagaro, J., Mandayam, S., & Polikar, R. (2004). Combining classifiers for multisensor data fusion. In 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 (pp. 1232-1237). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2). https://doi.org/10.1109/ICSMC.2004.1399793