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Improving classification performance for heterogeneous cancer gene expression data

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2 Author(s)
Fung, B.Y.M. ; Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China ; Ng, V.T.Y.

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.

Published in:

Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International Conference on  (Volume:2 )

Date of Conference:

5-7 April 2004