A multi-objective evolutionary algorithm for robust positive-unlabeled learning

Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are “clean”, which is not true in real applications due...

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Veröffentlicht in:	Information sciences Jg. 678; S. 120992
Hauptverfasser:	Qiu, Jianfeng, Tang, Qi, Tan, Ming, Li, Kaixuan, Xie, Juan, Cai, Xiaoqiang, Cheng, Fan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.09.2024
Schlagworte:	Dual-coding scheme Multi-objective evolutionary algorithm PU sample selection Robust PU learning Robust PU learning PU sample selection Multi-objective evolutionary algorithm Dual-coding scheme
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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