A multilevel tabu search algorithm for the feature selection problem in biomedical data

The automated analysis of patients’ biomedical data can be used to derive diagnostic and prognostic inferences about the observed patients. Many noninvasive techniques for acquiring biomedical samples generate data that are characterized by a large number of distinct attributes (i.e., features) and a small number of observed patients (i.e., samples). Using these biomedical data to derive reliable inferences, such as classifying a given patient as either cancerous or noncancerous, requires that the ratio r of the number of samples to the number of features be within the range 5