Use of multiple classifiers in classification of data from multiple data sources

Gunnar Jakob Briem; Jon Atli Benediktsson; Johannes R. Sveinsson

Use of multiple classifiers in classification of data from multiple data sources

Gunnar Jakob Briem^*, Jon Atli Benediktsson, Johannes R. Sveinsson

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

12 Citations (Scopus)

Abstract

Recently, multiple classifiers, which base their decision on the output from more than one classifier, have become popular. In this paper, the use of multiple classifiers in data fusion of multisource remote sensing and geographic data is studied. In particular, the paper focuses on the recently proposed methodologies of bagging and boosting. Bagging, boosting, and several versions of optimized statistical consensus theory are compared in classification of a multisource remote sensing and geographic data set. The results show boosting to outperform all the other methods in terms of test accuracies.

Original language	English
Pages	882-884
Number of pages	3
Publication status	Published - 2001
Event	2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001) - Sydney, NSW, Australia Duration: 9 Jul 2001 → 13 Jul 2001

Conference

Conference	2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001)
Country/Territory	Australia
City	Sydney, NSW
Period	9/07/01 → 13/07/01

Cite this

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title = "Use of multiple classifiers in classification of data from multiple data sources",

abstract = "Recently, multiple classifiers, which base their decision on the output from more than one classifier, have become popular. In this paper, the use of multiple classifiers in data fusion of multisource remote sensing and geographic data is studied. In particular, the paper focuses on the recently proposed methodologies of bagging and boosting. Bagging, boosting, and several versions of optimized statistical consensus theory are compared in classification of a multisource remote sensing and geographic data set. The results show boosting to outperform all the other methods in terms of test accuracies.",

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AU - Briem, Gunnar Jakob

AU - Benediktsson, Jon Atli

AU - Sveinsson, Johannes R.

PY - 2001

Y1 - 2001

N2 - Recently, multiple classifiers, which base their decision on the output from more than one classifier, have become popular. In this paper, the use of multiple classifiers in data fusion of multisource remote sensing and geographic data is studied. In particular, the paper focuses on the recently proposed methodologies of bagging and boosting. Bagging, boosting, and several versions of optimized statistical consensus theory are compared in classification of a multisource remote sensing and geographic data set. The results show boosting to outperform all the other methods in terms of test accuracies.

AB - Recently, multiple classifiers, which base their decision on the output from more than one classifier, have become popular. In this paper, the use of multiple classifiers in data fusion of multisource remote sensing and geographic data is studied. In particular, the paper focuses on the recently proposed methodologies of bagging and boosting. Bagging, boosting, and several versions of optimized statistical consensus theory are compared in classification of a multisource remote sensing and geographic data set. The results show boosting to outperform all the other methods in terms of test accuracies.

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M3 - Paper

AN - SCOPUS:0035574320

SP - 882

EP - 884

T2 - 2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001)

Y2 - 9 July 2001 through 13 July 2001

ER -

Use of multiple classifiers in classification of data from multiple data sources

Abstract

Conference

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