Jun Chin Ang

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Gene expression data always suffer from the high dimen-sionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually(More)
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do(More)
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