Genetic programming for feature construction and selection in classification on high-dimensional data

This work presents a comprehensive study to investigate the use of genetic programming using a tree-based representation for feature construction and selection on high-dimensional classification problems and shows that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases.