Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data.
With advances in high-throughput molecular techniques, researchers can study the expression of tens thousands of genes simultaneously. Cancer classification based on gene expression levels is one of the central problems in genome research. Logistic regression is a popular classification method and has an explicit statistical interpretation which can obtain probabilities of classification regarding the cancer phenotype. However, in most gene expression studies, the number of genes typically far exceeds the number of sample sizes. This situation is called high dimensional and low sample size problem and the normal logistic regression method cannot be directly used to obtain the parameter estimates. Thus, although logistic regression is one of the most popular classification methods, it does unfortunately not achieve the purpose of the feature selection.
There remains a strong need for methods and related systems sufficiently dealing with the problem of highly correlated variables and the high dimensional and low sample size problem, i.e. those which are suitable for feature selection in particular in the medical field. Clearly, having a fast and reliable analysis of features could significantly contribute to an improved diagnosis of diseases and treatment selection.