Feature Selection as a Preprocessing Step for Classification in Gene Expression Data

Many times, when studying gene expression data, unknown attributes, which can be redundant and even, in certain cases, irrelevant, are manipulated. The application of selection attributes algorithms as a preprocessing can help in the knowledge discovery database process. This paper is about applying selection attributes algorithms in two gene expression databases. The result shows that the use of these algorithms can improve the classification algorithms performance.