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A Model-Free Subject Selection Method for Active Learning Classification Procedures

A Model-Free Subject Selection Method for Active Learning Classification Procedures To construct a classification rule via an active learning method, during the learning process, users select training subjects sequentially, without knowing their labels, based on the model learned at the current stage. For a parametric-model-based classification rule, methods of statistical experimental design are popular guidelines for selecting new learning subjects. However, there is a lack of a counterpart for non-parametric-model-based classifiers, such as support vector machines. Thus, we propose a subject selection scheme via an extended influential index for the area under a receiver operating characteristic curve, which is applicable to general classifiers with continuous scores. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

A Model-Free Subject Selection Method for Active Learning Classification Procedures

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References (16)

Publisher
Springer Journals
Copyright
Copyright © The Classification Society 2021
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-021-09388-3
Publisher site
See Article on Publisher Site

Abstract

To construct a classification rule via an active learning method, during the learning process, users select training subjects sequentially, without knowing their labels, based on the model learned at the current stage. For a parametric-model-based classification rule, methods of statistical experimental design are popular guidelines for selecting new learning subjects. However, there is a lack of a counterpart for non-parametric-model-based classifiers, such as support vector machines. Thus, we propose a subject selection scheme via an extended influential index for the area under a receiver operating characteristic curve, which is applicable to general classifiers with continuous scores.

Journal

Journal of Classification – Springer Journals

Published: Oct 1, 2021

Keywords: Active learning; Subject selection; Classification; Influential index; ROC curve; AUC

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