Alternative feature selection with user control

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which does not suffice in certain scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. Next, we analyze the complexity of this optimization problem and show $$\mathcal{N}\mathcal{P}$$ N P -hardness. Further, we discuss how to integrate conventional feature-selection methods as objectives. Finally, we evaluate alternative feature selection in comprehensive experiments with 30 datasets representing binary-classification problems. We observe that alternative feature sets may indeed have high prediction quality, and we analyze factors influencing this outcome.