An Empirical Research of Multi-Classifier Fusion Methods and Diversity Measure in Remote Sensing Classification

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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

Adelaide, Australia

January 23-January 24

ISBN: 0-7695-3090-7

	ASCII Text	x
	Hongchao Ma, Wei Zhou, Xinyi Dong, Honggen Xu, "An Empirical Research of Multi-Classifier Fusion Methods and Diversity Measure in Remote Sensing Classification," International Workshop on Knowledge Discovery and Data Mining, pp. 90-93, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.

	BibTex	x
	@article{ 10.1109/WKDD.2008.66, author = {Hongchao Ma and Wei Zhou and Xinyi Dong and Honggen Xu}, title = {An Empirical Research of Multi-Classifier Fusion Methods and Diversity Measure in Remote Sensing Classification}, journal ={International Workshop on Knowledge Discovery and Data Mining}, volume = {0}, year = {2008}, isbn = {0-7695-3090-7}, pages = {90-93}, doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.66}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - International Workshop on Knowledge Discovery and Data Mining TI - An Empirical Research of Multi-Classifier Fusion Methods and Diversity Measure in Remote Sensing Classification SN - 0-7695-3090-7 SP90 EP93 A1 - Hongchao Ma, A1 - Wei Zhou, A1 - Xinyi Dong, A1 - Honggen Xu, PY - 2008 VL - 0 JA - International Workshop on Knowledge Discovery and Data Mining ER -

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.66

In this paper, Multi-Classifier System (MCS) is applied to the automatic classification of remote sensing images, and some effective multi-classifier fusion methods with relatively high accuracy are proposed based on substantive experiments. The classification accuracy of MCS has been remarkably improved compared to single classifier with an average increment of 5%. In addition, a diversity measure named EPD is presented, and the paper proves that its ability in predicting the performance of classifiers combining can be used to assist the construction of multiple classifier systems.

Citation:

Hongchao Ma, Wei Zhou, Xinyi Dong, Honggen Xu, "An Empirical Research of Multi-Classifier Fusion Methods and Diversity Measure in Remote Sensing Classification," wkdd, pp.90-93, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008

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