Multiple instance learning

In <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, multiple-instance learning (MIL) is a type of <a href="/facts/Supervised_learning/JlNFNPFt">supervised learning</a>. Instead of receiving a set of instances which are individually <a href="/facts/Labeled_data/o0doUSPA">labeled</a>, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance <a href="/facts/Binary_classification/MeYYAMwp">binary classification</a>, a bag may be labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at least one instance in it which is positive. From a collection of labeled bags, the learner tries to either (i) induce a concept that will label individual instances correctly or (ii) learn how to label bags without inducing the concept.
Babenko (2008) gives a simple example for MIL. Imagine several people, and each of them has a key chain that contains few keys. Some of these people are able to enter a certain room, and some aren't. The task is then to predict whether a certain key or a certain key chain can get you into that room. To solve this problem we need to find the exact key that is common for all the "positive" key chains. If we can correctly identify this key, we can also correctly classify an entire key chain - positive if it contains the required key, or negative if it doesn't.

Multiple instance learning open-in-new

Multiple instance learning