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

Bo-Shiang Ke and Yuan-chin Ivan Chang ()
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Bo-Shiang Ke: National Chiao Tung University
Yuan-chin Ivan Chang: Academia Sinica

Journal of Classification, 2021, vol. 38, issue 3, No 7, 544-555

Abstract: 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.

Keywords: Active learning; Subject selection; Classification; Influential index; ROC curve; AUC (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00357-021-09388-3

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