Support Vector Machines and Relevance Vector Machines and Their Usage
Journal Title: Enliven: Journal of Anesthesiology and Critical Care Medicine - Year 2018, Vol 5, Issue 1
Abstract
The researchers sought to use to supervised machine learning algorithms to determine the minor set of genes that can provide exact cancer from the microarray data. The importance of getting the minimum subset significantly stimulate the need for additional studies on the likely biological relationship that between the small number of genes together with the onset of cancer and drug development; it helps to considerably reduce the cost incurred in testing cancer since it streamlines the gene expression tests to cover a minimal amount of genes. In addition, a minimal subset helps reduce noise arising from extraneous genes and the associated computational problem [4]. The researchers have suggested a method that includes two major steps such as using the Analysis of Variance (ANOVA) ranking scheme to select the most significant genes and a superior classifier was used to determine all the modest combinations of the main genes. The researchers used both the Relevance Vector Machine (RVM) classifier and the Support Vector Machine (SVM) to increases the accuracy of cancer genes classification and the drug prediction. The findings of the experiment prove that the suggested method performs excellently or it achieves accurate cancer classification in comparison to the common conventional techniques.
Authors and Affiliations
Ray Nick
Keywords
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