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. 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.
Machine learning Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued. MIL deals with problems with incomplete knowledge of labels in training sets. More precisely, in multiple-instance learning, the training set consists of labeled "bags", each of which is a collection of unlabeled instances. A bag is positively labeled if at least one instance in it is positive, and is negatively labeled if all instances in it are negative. The goal of the MIL is to predict the labels of new, unseen bags.
History Keeler et al., in his work in the early 1990s was the first one to explore the area of MIL. 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. They tried to create a learning system that could predict whether new molecule was qualified to make some drug, or not, through analyzing a collection of known molecules. Molecules can have many alternative low-energy states, but only one, or some of them, are qualified to make a drug. 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. Dietterich et al. 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. 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. Thus formulating multiple-instance learning. Solution to the multiple instance learning problem that Dietterich et al. proposed is the axis-parallel rectangle (APR) algorithm. It attempts to search for appropriate axis-parallel rectangles constructed by the conjunction of the features. They tested the algorithm on Musk dataset, which is a concrete test data of drug activity prediction and the most popularly used benchmark in multiple-instance learning. APR algorithm achieved the best result, but APR was designed with Musk data in mind. 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. 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. Multiple instance learning can be used to learn the properties of the subimages which characterize the target scene. From there on, these frameworks have been applied to a wide spectrum of applications, ranging from image concept learning and text categorization, to stock market prediction.
Examples Take image classification for example Amores (2013). Given an image, we want to know its target class based on its visual content. For instance, the target class might be "beach", where the image contains both "sand" and "water". In MIL terms, the image is described as a bag X = { X 1 , . . , X N } {\displaystyle X=\{X_{1},..,X_{N}\}} , where each X i {\displaystyle X_{i}} is the feature vector (called instance) extracted from the corresponding i {\displaystyle i} -th region in the image and N {\displaystyle N} is the total regions (instances) partitioning the image. The bag is labeled positive ("beach") if it contains both "sand" region instances and "water" region instances. Examples of where MIL is applied are:
Molecule activity Predicting binding sites of Calmodulin binding proteins Predicting function for alternatively spliced isoforms Li, Menon & et al. (2014),Eksi et al. (2013) Image classification Maron & Ratan (1998) Text or document categorization Kotzias et al. (2015) Predicting functional binding sites of MicroRNA targets Bandyopadhyay, Ghosh & et al. (2015) Medical image classificationZhu et al. (2017)P.J.Sudharshan et al. (2019) Numerous researchers have worked on adapting classical classification techniques, such as support vector machines or boosting, to work within the context of multiple-instance learning.
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