Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
This addresses the challenge of assessing feature selection quality for researchers, though it appears incremental as it builds on existing synthetic feature generation techniques.
The paper tackles the problem of evaluating feature selection algorithms by proposing a method to automatically generate complex, redundant features, which are difficult to create synthetically, and initial experiments show it can produce such features for use in benchmark datasets.
Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life. A plethora of feature selection algorithms have been proposed, but it is difficult to truly analyse the quality of a given algorithm. Ideally, an algorithm would be evaluated by measuring how well it removes known bad features. Acquiring datasets with such features is inherently difficult, and so a common technique is to add synthetic bad features to an existing dataset. While adding noisy features is an easy task, it is very difficult to automatically add complex, redundant features. This work proposes one of the first approaches to generating redundant features, using a novel genetic programming approach. Initial experiments show that our proposed method can automatically create difficult, redundant features which have the potential to be used for creating high-quality feature selection benchmark datasets.