Privileged information learning with weak labels

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dc.authorid0000-0002-7201-6963
dc.authorid0000-0003-1840-9958
dc.contributor.authorXiao, Yanshan
dc.contributor.authorYe, Zexin
dc.contributor.authorZhao, Liang
dc.contributor.authorKong, Xiangjun
dc.contributor.authorLiu, Bo
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.date.accessioned2024-09-25T19:59:50Z
dc.date.available2024-09-25T19:59:50Z
dc.date.issued2023
dc.departmentBAİBÜ, Teknoloji Fakültesi, Elektrik Elektronik Mühendisliği Bölümü
dc.description.abstractPrivileged information learning is proposed to construct the classifier by incorporating privileged knowledge. At present, most of the privileged information learning methods assume that the instance is accurately labeled. However, in real-world applications, an instance may be weakly labeled. In this paper, we propose a novel privileged information learning method with weak labels (PLWB). The hypothesis of our work is that an instance may be annotated by a number of labelers and different labelers may give different labels to this instance due to distinct professional knowledge and subjective factors. It leads to ambiguous labels of instances, namely weak labels. To solve this problem, our methodology is to give each labeler a weight and incorporate these weights into a privileged information learning model. Our technique is to employ a heuristic framework to optimize the labeler weights and the privileged information learning model jointly. The existing privileged information learning methods do not consider the weak label problem, and assign an equal or random weight to each labeler. Our work is different from these methods. The novelty and theoretical contribution is that this is the first work to deal with the weak label problem in privileged information learning. The merit is that we assign an unknown weight to each labeler and solve the optimal values of these weights in the optimization process, such that the performance of the learning model can be improved with the optimal labeler weights. In the experiments, the tool that we use is MATLAB, in which we implement our algorithm. The experimental datasets include one handwritten categorization dataset, two image classification datasets (i.e., Animals-with-Attributes dataset and Caltech-101 dataset), and one disease diagnosis dataset (i.e., Alzheimer's Disease Neuroimaging Initiative dataset), in which the number of instances used is 2000, 6180, 8677 and 202, respectively. The obtained results are that: (1) by optimizing the labeler weights, the proposed PLWB method obtains explicitly higher classification accuracy than the existing privileged information learning methods; (2) PLWB has relatively higher training time since it needs to solve the labeler weights in the optimization process.& COPY; 2023 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipNatural Science Foundation of China [2023A1515012560]; Guangdong Natural Science Foundation; [62076074]en_US
dc.description.sponsorshipThe authors would like to thank the reviewers for their very useful comments and suggestions. This work was supported in part by the Natural Science Foundation of China under Grant 62076074, in part by Guangdong Natural Science Foundation under Grant 2023A1515012560.en_US
dc.identifier.doi10.1016/j.asoc.2023.110298
dc.identifier.endpage13
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85154039602en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.110298
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13945
dc.identifier.volume142en_US
dc.identifier.wosWOS:001054450100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectPrivileged Information Learningen_US
dc.subjectWeak Labelen_US
dc.subjectSupport Vector Machineen_US
dc.subjectHeuristic Frameworken_US
dc.subjectLabeler Re-weightingen_US
dc.titlePrivileged information learning with weak labelsen_US
dc.typeArticleen_US

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