Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23846
Title: Improving classification performance for heterogeneous cancer gene expression data
Authors: Fung, BYM
Ng, VTY 
Keywords: Cancer
Classification
Lung
Medical administrative data processing
Issue Date: 2004
Publisher: IEEE
Source: ITCC 2004 : International Conference on Information Technology, Coding and Computing : proceedings : April 5-7, 2004, Las Vegas, Nevada, v. 2, p. 131-132 How to cite?
Abstract: In our previous work, we proposed the "impact factors" (IFs) to measure the symmetric errors in different microarray experiments, and integrated the IFs to the Golub and Slonim (GS) and k-nearest neighbors (kNN) classifiers. In this paper, we perform experiments with different cancer types, which are lung adenocarcinomas and prostate cancer, to further validate the efficiency and effectiveness of the IFs integrations in terms of measurements of classification accuracy, sensitivity and specificity. For both cancer types, the IFs integrations can be successfully improved on the classification performance.
URI: http://hdl.handle.net/10397/23846
ISBN: 0-7695-2108-8
DOI: 10.1109/ITCC.2004.1286608
Appears in Collections:Conference Paper

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