Support vector machines with genetic fuzzy feature transformation for biomedical data classification

In this paper, we present a genetic fuzzy feature transformation method for support vector machines (SVMs) to do more accurate data classification. Given data are first transformed into a high feature space by a fuzzy system, and then SVMs are used to map data into a higher feature space and then construct the hyperplane to make a final decision. Genetic algorithms are used to optimize the fuzzy feature transformation so as to use the newly generated features to help SVMs do more accurate biomedical data classification under uncertainty. The experimental results show that the new genetic fuzzy SVMs have better generalization abilities than the traditional SVMs in terms of prediction accuracy.