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A novel observation points‐based positive‐unlabeled learning algorithm

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1425-1443页

作者： Yulin He Xu Li Manjing Zhang Philippe Fournier‐Viger Joshua Zhexue Huang Salman Salloum Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) ShenzhenChina College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Computing National University of SingaporeSingaporeSingapore

In this study,an observation points‐based positive‐unlabeled learning algorithm(hence called OP‐PUL)is proposed to deal with positive‐unlabeled learning(PUL)tasks by judiciously assigning highly credible labels to unlabeled *** proposed OP‐PUL algorithm has three ***,an observation point classifier ensemble(OPCE)algorithm is constructed to divide unlabeled samples into two categories,which are temporary positive and permanent negative ***,a temporary OPC(TOPC)is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive ***,a permanent OPC(POPC)is finally trained based on the combination of original positive samples,permanent positive samples and permanent negative *** exhaustive experimental evaluation is conducted to validate the feasibility,rationality and effectiveness of the OP‐PUL algorithm,using 30 benchmark PU data *** show that(1)the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and(2)the permanent positive samples have a consistent probability distribution with the original positive ***,a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL *** demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

关键词： artificial intelligence datamining,machine learning

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