The bag is labeled positive ("beach") if it contains both "sand" region instances and "water" region instances.Examples of where MIL is applied are: 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.For instance, the target class might be "beach", where the image contains both "sand" and "water".
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.
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.
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 goal of the MIL is to predict the labels of new, unseen bags.Keeler et al., in his work in the early 1990s was the first one to explore the area of MIL.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.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.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.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.