Implementation of and experimental software for active selection of classification features
Thomas T. Kok, Georg Krempl, Hugo Gerard Schnack · Software Impacts · 2021
In some machine learning applications, obtaining data on the most predictive features is costly, but other features are readily available. Recently, first active learning approaches for this A ctively S electing C lassification F eatures problem ( ASCF ) have been proposed. In this paper, we introduce a Python package that provides a framework for ASCF, including implementations of a supervised and an unsupervised selection approach, as well as a framework for performing experimental evaluations. This framework has been used in recent publications in the context of neuroimaging research on mental disorders, where its usefulness has been demonstrated in a simulated study design with MRI data.