Comparison of Different Feature Extraction Methods on Classification of Gene Expression Data

It is important to extract the most relevant features of the genetic profiles to determine the health condition of the cellular structure. Early diagnosis of the illnesses has a great importance in the treatment. In this study, we analyzed a gene expression data by classifying using support vector machines after applying different feature extraction methods as principal component analysis (PCA) and independent component analysis (ICA). Results have been compared with the results of the feature extraction algorithm based on genetic algorithm (GA).