Active learning in multiple-class classification problems via individualized binary models

We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As in...

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Bibliographic Details
Published in:Computational statistics & data analysis Vol. 145; p. 106911
Main Authors: Li, Jingjing, Chen, Zimu, Wang, Zhanfeng, Chang, Yuan-chin Ivan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2020
Subjects:
A-optimality design
algorithms
experimental design
Individualized binary estimation
learning
Multiple-class classification
sample size
Sequential estimation
Stopping time
Multiple-class classification
Stopping time
Sequential estimation
Individualized binary estimation
A-optimality design
ISSN:0167-9473, 1872-7352
Online Access:Get full text
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Summary:We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As information on the training sample sizes needed to obtain satisfactory performance is lacking, we adopt an adaptive subject recruiting scheme with an experimental design criterion to shorten the training process. Because this kind of active learning method is naturally conducted in a sequential manner, we adopt sequential analysis to control the required sample size and ensure the performance of the final classifier. Additionally, we report its statistical properties and numerical results from using synthesized and real data.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2020.106911