Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging. Current methods often struggle with scalability, limiting their applicability to large datasets, or make restrictive assumptions about feature-property relationships, hindering their ability to capture complex interactions. BoUTS's general and scalable feature selection algorithm surpasses these limitations by identifying both universal features relevant to all datasets and task-specific features predictive for specific subsets. Evaluated on seven diverse chemical regression datasets, BoUTS achieves state-of-the-art feature sparsity while generally maintaining prediction accuracy comparable to specialized methods. Notably, BoUTS's universal features enable domain-specific knowledge transfer between datasets, and we expect these results to be broadly useful to manually-guided inverse problems. Beyond its current application, BoUTS holds potential for elucidating data-poor systems by leveraging information from similar data-rich systems.Scientific Contribution: BoUTS selects nonlinear, universally informative features across multiple datasets. We identify crucial "universal features" across seven real-world chemistry datasets, which enhance cross-dataset interpretability and selection stability. BoUTS is highly scalable and is applicable to tabular data from many domains, and our results identify connections between seemingly unrelated chemical domains.
Raymond et al. (Sat,) studied this question.