Exploring Robustness in a Combined Feature Selection Approach (Best Paper Award)

A crucial task in the bidding phase of industrial systems is a precise prediction of the number of hardware components of specific types for the proposal of a future project. Linear regression models, trained on data of past projects, are efficient in supporting such decisions. The number of features used by these regression models should be as small as possible, so that determining their quantities generates minimal effort. The fact that training data are often ambiguous, incomplete, and contain outlier makes challenging demands on the robustness of the feature selection methods used. We present a combined feature selection approach: (i) iteratively learn a robust well-fitted statistical model and rule out irrelevant features, (ii) perform redundancy analysis to rule out dispensable features. In a case study from the domain of hardware management in Rail Automation we show that this approach assures robustness in the calculation of hardware components.