For cancer classification problems based on gene expression, the data usually has only a
few dozen sizes but has thousands to tens of thousands of genes which could contain a …

Gene expression data always suffer from the high dimensionality issue, therefore feature
selection becomes a fundamental tool in the analysis of cancer classification. Basically, the …

Extracting a subset of informative genes from microarray expression data is a critical data
preparation step in cancer classification and other biological function analyses. Though …

Gene selection methods available have high computational complexity. This paper applies
an 1-norm support vector machine with the squared loss (1-norm SVMSL) to implement fast …

The application of gene expression data to the diagnosis and classification of cancer has
become a hot issue in the field of cancer classification. Gene expression data usually …

Simultaneous multiclass classification of tumor types is essential for future clinical
implementations of microarray-based cancer diagnosis. In this study, we have combined …

In a DNA microarray dataset, gene expression data often has a huge number of features
(which are referred to as genes) versus a small size of samples. With the development of …

Motivation: Given the thousands of genes and the small number of samples, gene selection
has emerged as an important research problem in microarray data analysis. Support Vector …

Motivation: Cancer diagnosis is one of the most important emerging clinical applications of
gene expression microarray technology. We are seeking to develop a computer system for …

Background Selecting a parsimonious set of informative genes to build highly generalized
performance classifier is the most important task for the analysis of tumor microarray …