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The problem arose because scientists could only determine if molecule is qualified, or not, but they couldn't say exactly which of its low-energy shapes are responsible for that.One of the proposed ways to solve this problem was to use supervised learning, and regard all the low-energy shapes of the qualified molecule as positive training instances, while all of the low-energy shapes of unqualified molecules as negative instances. showed that such method would have a high false positive noise, from all low-energy shapes that are mislabeled as positive, and thus wasn't really useful.
The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al.
while they were investigating the problem of drug activity prediction.
Given an image, an instance is taken to be one or more fixed-size subimages, and the bag of instances is taken to be the entire image.
An image is labeled positive if it contains the target scene - a waterfall, for example - and negative otherwise.
If the space of instances is make the assumption regarding the relationship between the instances within a bag and the class label of the bag.
Because of its importance, that assumption is often called standard MI assumption.
Their approach was to regard each molecule as a labeled bag, and all the alternative low-energy shapes of that molecule as instances in the bag, without individual labels. Solution to the multiple instance learning problem that Dietterich et al.
proposed is three axis-parallel rectangle (APR) algorithm.
Problem of multi-instance learning is not unique to drug finding.
In 1998, Maron and Ratan found another application of multiple instance learning to scene classification in machine vision, and devised Diverse Density framework.