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Bioinformatics Advance Access originally published online on January 27, 2006
Bioinformatics 2006 22(8):981-988; doi:10.1093/bioinformatics/btl027
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Enhancing instance-based classification with local density: a new algorithm for classifying unbalanced biomedical data

Claudia Plant 1, Christian Böhm 2, Bernhard Tilg 1 and Christian Baumgartner 1,*

1 Research Group for Clinical Bioinformatics, Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology Hall in Tyrol, Austria
2 Institute for Computer Science, University of Munich Germany

*To whom correspondence should be addressed.

Motivation: Classification is an important data mining task in biomedicine. In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. Even more important than the overall accuracy is the balance of a classifier, particularly if datasets of unbalanced class size are examined.

Results: We present a novel instance-based classification technique which takes both information of different local density of data objects and local cluster structures into account. Our method, which adopts the basic ideas of density-based outlier detection, determines the local point density in the neighborhood of an object to be classified and of all clusters in the corresponding region. A data object is assigned to that class where it fits best into the local cluster structure. The experimental evaluation on biomedical data demonstrates that our approach outperforms most popular classification methods.

Availability: The algorithm LCF is available for testing under http://biomed.umit.at/upload/lcfx.zip

Contact: christian.baumgartner{at}umit.at

Received on September 29, 2005; revised on January 3, 2006; accepted on January 25, 2006

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