Genetic algorithms for feature selection in Data Analytics

Many common applications of predictive analytics, from customer segmentation to medical diagnosis, arise from complex relationships between features (also called variables or characteristics). Feature selection is the process of finding the most relevant variables for a predictive model. These techniques can be used to identify and remove unneeded, irrelevant and redundant features that do not contribute or decrease the accuracy of the predictive model. Mathematically, feature selection is formulated as a combinatorial optimization problem. Here the function to optimize is the generalization performance of the predictive model, represented by the error on a selection data set.