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Multi-instance Classification

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Multiple Instance Learning

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Abstract

In the machine-learning community, the most widely used MIL paradigm is Multi-Instance Classification (MIC). Most contributions in MIL are related to this predictive task and a considerable number of problems have been solved successfully. In Sects. 3.1 and 3.2, we introduce the MIC problem, give a formal definition, and describe the evaluation metrics. Section 3.3 recalls a general taxonomy, describing the main categories established within MIC. An in-depth study of the different methods in each category is made in later chapters. In Sects. 3.4 and 3.5, we discuss two specific design aspects related to MIC algorithms. In the former, we present the different assumptions that can be used to relate class labels of instances within a bag to the class label of the bag itself. The latter section describes the main distance metrics that allow to determine similarity between bags. We conclude this chapter by listing common MIC case studies found in the literature in Sect. 3.6 as well as the relevant MIC software tools in Sect. 3.7.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

  2. Department of Computer Science, University of Córdoba, Córdoba, Spain

    Sebastián Ventura

  3. Center of Information Studies, Central University “Marta Abreu” of Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Chris Cornelis & Sarah Vluymans

  5. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Amelia Zafra

  6. Central University “Marta Abreu” of Las Villas, Santa Clara, Villa Clara, Cuba

    Dánel Sánchez-Tarragó

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  1. Francisco Herrera
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  2. Sebastián Ventura
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  3. Rafael Bello
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  4. Chris Cornelis
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  5. Amelia Zafra
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  6. Dánel Sánchez-Tarragó
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  7. Sarah Vluymans
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Correspondence to Francisco Herrera .

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Herrera, F. et al. (2016). Multi-instance Classification. In: Multiple Instance Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-47759-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-47759-6_3

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