Survey of Multi Instance learning Algorithms

Abstract: In multi-instance learning, the training set comprises labelled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. The Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. The supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labelled data. However, learning from bags raises important challenges that are unique to MIL. This paper provides a complete survey of the characteristics which define and distinguish the types of MIL problems. Until now, these problem characteristics have not been properly identified and described. There are some types learning generally in ML algorithms like, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Some important issues to be addressed in this paper. Finally delivers insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking.

Keywords: Multiple instance learning, weakly labelled data, ambiguity of instance labels