Active Selection of Classification Features
Kok, Thomas T.; Brouwer, Rachel M.; Mandl, Rene M.; Schnack, Hugo G.; Krempl, Georg
(2021)
Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Proceedings, volume 12695, pp. 184 - 195
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12695, pp. 184 - 195
19th International Symposium on Intelligent Data Analysis, IDA 2021, volume 12695, pp. 184 - 195
(Part of book)
Abstract
Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic
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Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs. Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model f:x→y to explain/predict the relationship between an expensive-to-acquire set of variables x and a class label y. Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task’s performance most when acquiring their expensive features x and including them to the primary training set. We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data.
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Keywords: Active class selection, Active feature acquisition, Active feature selection, Active learning, Classification, Semi-supervised learning, Theoretical Computer Science, General Computer Science
ISSN: 0302-9743
ISBN: 9783030742508
978-3-030-74251-5
Publisher: Springer Science and Business Media Deutschland GmbH
Note: Funding Information: Acknowledgements. We would like to thank Ad Feelders for valuable discussions on this topic. Furthermore, we would like to thank the SIG Applied Data Science at UU/UMCU for funding the research project “Using active learning to reduce the costs of population-based neuroimaging studies”. Publisher Copyright: © 2021, Springer Nature Switzerland AG.
(Peer reviewed)