Deep multiple instance selection

Multiple-Instance Learning: Multiple Feature Selection on Instance Representation

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent a...

Attention-based Deep Multiple Instance Learning

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator tha...

Multiple-Instance Learning with Instance Selection via Dominant Sets

Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supe...

Integrating Deep Learning and Multiple Instance Learning

Robust multiple-instance learning ensembles using random subspace instance selection

Many real-world pattern recognition problems can be modeled using multiple-instance learning (MIL), where instances are grouped into bags, and each bag is assigned a label. State-of-the-art MIL methods provide a high level of performance when strong assumptions are made regarding the underlying data distributions, and the proportion of positive to negative instances in positive bags. In this pa...