Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets

arXiv:2403.14466v1 Announce Type: new
Abstract: 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 to identify 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 …

abstract algorithm arxiv assumptions cs.lg current datasets domains feature features feature selection general interactions interpretability large datasets property relationships restrictive scalability scalable scientific struggle type universal