Abstract
Regression is a popular machine learning task that aims to predict a numerical outcome. In multi-instance regression (MIR), each observation can be described by several instances. After a brief introduction to this topic in Sect. 6.1, we present a formal definition of MIR and its appropriate evaluation measures in Sect. 6.2. We organize the MIR methods in two main categories. Algorithms that focus on individual instances of each bag in their construction of a regression model are examined in Sect. 6.3, while Sect. 6.4 discusses methods that treat bags as single entities to create a regression model operating at the bag level. Section 6.5 lists some summarizing remarks.
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Herrera, F. et al. (2016). Multi-instance Regression. In: Multiple Instance Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-47759-6_6
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DOI: https://doi.org/10.1007/978-3-319-47759-6_6
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