Multiple classifier systems for multisensor data fusion

Robi Polikar, Devi Parikh, Shreekanth Mandayam

Research output: Contribution to conferencePaperpeer-review

21 Scopus citations


We have previously introduced Learn++, an ensemble of classifiers based algorithm capable of incremental learning from additional data, and pointed to its feasibility in data fusion applications. In this contribution, we provide additional details, updated results and insight on how such a system can be used in integrating complementary knowledge provided by different data sources obtained from different sensors. Essentially, the algorithm generates an ensemble of classifiers using data from each source, and combines these classifiers using a weighted voting procedure. The weights are determined based on the individual classifier's training performance as well as the observed or predicted reliability of each data source.

Original languageEnglish (US)
Number of pages5
StatePublished - Nov 21 2006
Event2006 IEEE Sensors Applications Symposium - Houston, TX, United States
Duration: Feb 7 2006Feb 9 2006


Other2006 IEEE Sensors Applications Symposium
Country/TerritoryUnited States
CityHouston, TX

All Science Journal Classification (ASJC) codes

  • Engineering(all)


Dive into the research topics of 'Multiple classifier systems for multisensor data fusion'. Together they form a unique fingerprint.
  • Virtual Reality Lab

    Shreekanth Mandayam (Manager) & George D. Lecakes (Manager)

    Equipment/facility: Facility

Cite this