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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Veröffentlicht in:	Computational statistics & data analysis Jg. 145; S. 106911
Hauptverfasser:	Li, Jingjing, Chen, Zimu, Wang, Zhanfeng, Chang, Yuan-chin Ivan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2020
Schlagworte:	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-Zugang:	Volltext
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