Support vector machines with evolutionary feature weights optimization for biomedical data classification

In support vector machines (SVMs) learning, data to be classified are directly fed to the algorithms without modification. In many real world applications, objects however cannot be represented by original feature vectors accurately because the original features of vectors might contain noise, imprecise description, or unrelated information, which negatively affect SVMs to learn useful knowledge from raw given data. To challenging this problem, we in this paper present an evolutionary feature weights optimization method, which is used to transform the raw data into a "better" feature space to improve SVMs classification accuracies.