Multiple Instance Learning (MIL) Algorithm

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A Multiple Instance Learning (MIL) Algorithm is a Supervised Learning Algorithm that can be implemented by a Multiple Instance Learning System to solve a Multiple Instance Learning Task.



References

2019

  • (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Multiple_instance_learning Retrieved:2019-2-3.
    • In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, 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.

      Convenient and simple example for MIL was given in.[1] 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.

  • Babenko, Boris. “Multiple instance learning: algorithms and applications." View Article PubMed/NCBI Google Scholar (2008).
  • 2017

    2014

    • http://www.kyb.mpg.de/bs/people/pgehler/mil/mil.html
      • Multiple Instance Learning (MIL) is a special learning framework which deals with uncertainty of instance labels. In this setting training data is available only as pairs of bags of instances with labels for the bags. Instance labels remain unknown and might be inferred during learning. A positive bag label indicates that at least one instance of that bag can be assigned a positive label. This instance can therefore be thought of as a witness for the label. Instance in negative labelled bags are altogether of the negative class, so there is no uncertainty about their label.
      • There exist quite an amount of literature to the Multiple Instance Learning problem. This website provides an overview of the MIL related research at this institute and hosts software we made available as well as datasets.

    2003

    • (Andrews et al., 2003) ⇒ S. Andrews, I. Tsochantaridis, and T. Hofmann. (2003). “Support Vector Machines for Multiple-Instance Learning].” In: Advances in Neural Information Processing Systems (NIPS 2003).

    1998