Chien-Pang Lee

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Recently, microarray technology has widely used on the study of gene expression in cancer diagnosis. The main distinguishing feature of microarray technology is that can measure thousands of genes at the same time. In the past, researchers always used parametric statistical methods to find the significant genes. However, microarray data often cannot obey(More)
Classification is a supervised machine learning procedure in which the effective model is constructed for prediction. The accuracy of classification mainly depends on the type of features and the characteristics of the dataset. Feature selection is an efficient approach in searching the most descriptive features which would contribute to the increase in the(More)
Due to the steep price of a microarray experiment, a microarray dataset usually contains a few experimental samples. While the number of experimental samples is small, the number of genes in an experimental sample is quite large. The fact that only a few of the large amount of genes are relevant to a diagnosis poses a challenge to the application of the(More)
For microarray data analysis, most of them focus on selecting relevant genes and calculating the classification accuracy by the selected relevant genes. This paper wants to detect the relation between the gene expression levels and the classes of a cancer (or a disease) to assist researchers for initial diagnosis. The proposed method is called a Two Stages(More)