Multiple instance classification via quadratic programming

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dc.contributor.authorKüçükaşçı, Emel Şeyma
dc.contributor.authorBaydoğan, Mustafa Gökçe
dc.contributor.authorTaşkın, Z. Caner
dc.date.accessioned2022-09-29T11:42:10Z
dc.date.available2022-09-29T11:42:10Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractMultiple instance learning (MIL) is a variation of supervised learning, where data consists of labeled bags and each bag contains a set of instances. Unlike traditional supervised learning, labels are not known for the instances in MIL. Existing approaches in the literature make use of certain assumptions regarding the instance labels and propose mixed integer quadratic programs, which introduce computational difficulties. In this study, we present a novel quadratic programming (QP)-based approach to classify bags. Solution of our QP formulation links the instance-level contributions to the bag label estimates, and provides a linear bag classifier along with a decision threshold. Our approach imposes no additional constraints on relating instance labels to bag labels and can be adapted to learning applications with different MIL assumptions. Unlike existing specialized heuristic approaches to solve previous MIL formulations, our QP models can be directly solved to optimality using any commercial QP solver. Also, kindly confirm Our computational experiments show that proposed QP formulation is efficient in terms of solution time, overcoming a main drawback of previous optimization algorithms for MIL. We demonstrate the classification success of our approach compared to the state-of-the-art methods on a wide range of real world datasets.en_US
dc.identifier.doi10.1007/s10898-021-01120-0en_US
dc.identifier.endpage670en_US
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85122892502en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage639en_US
dc.identifier.urihttps://hdl.handle.net/11467/5351
dc.identifier.urihttps://doi.org/10.1007/s10898-021-01120-0
dc.identifier.volume83en_US
dc.identifier.wosWOS:000741634100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Global Optimizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectMultiple instance learningen_US
dc.subjectClassificationen_US
dc.subjectQuadratic programmingen_US
dc.titleMultiple instance classification via quadratic programmingen_US
dc.typeArticleen_US

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