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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Vydané v:	Computational statistics & data analysis Ročník 145; s. 106911
Hlavní autori:	Li, Jingjing, Chen, Zimu, Wang, Zhanfeng, Chang, Yuan-chin Ivan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.05.2020
Predmet:	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
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