Abstract
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 Actively Selecting Classification Features 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.
| Original language | English |
|---|---|
| Article number | 100103 |
| Pages (from-to) | 1-3 |
| Journal | Software Impacts |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 2021 |
Bibliographical note
DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Keywords
- Active learning
- Active feature acquisition
- Active selection of classification features
- Machine learning experiment evaluation framework