Multiple instance classification via quadratic programming

Multiple 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 assum...

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Vydáno v:	Journal of global optimization Ročník 83; číslo 4; s. 639 - 670
Hlavní autoři:	Küçükaşcı, Emel Şeyma, Baydoğan, Mustafa Gökçe, Taşkın, Z. Caner
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2022 Springer Springer Nature B.V
Témata:	Algorithms Classification Computer Science Heuristic methods Labels Mathematical optimization Mathematics Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Quadratic programming Real Functions Supervised learning Quadratic programming Multiple instance learning Classification
ISSN:	0925-5001, 1573-2916
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Abstract	Multiple 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.
AbstractList	Multiple 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.
Audience	Academic
Author	Küçükaşcı, Emel Şeyma Baydoğan, Mustafa Gökçe Taşkın, Z. Caner
Author_xml	– sequence: 1 givenname: Emel Şeyma surname: Küçükaşcı fullname: Küçükaşcı, Emel Şeyma email: eskucukasci@ticaret.edu.tr organization: Department of Industrial Engineering, Istanbul Commerce University – sequence: 2 givenname: Mustafa Gökçe surname: Baydoğan fullname: Baydoğan, Mustafa Gökçe organization: Department of Industrial Engineering, Boğaziçi University – sequence: 3 givenname: Z. Caner surname: Taşkın fullname: Taşkın, Z. Caner organization: Department of Industrial Engineering, Boğaziçi University
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Snippet	Multiple instance learning (MIL) is a variation of supervised learning, where data consists of labeled bags and each bag contains a set of instances. Unlike...
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SubjectTerms	Algorithms Classification Computer Science Heuristic methods Labels Mathematical optimization Mathematics Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Quadratic programming Real Functions Supervised learning
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Title	Multiple instance classification via quadratic programming
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