Multiple Instance Learning: A Survey of Problem Characteristics and Applications

TL;DR

This survey comprehensively reviews multiple instance learning (MIL), identifying key problem characteristics, analyzing their impact on algorithm performance, and providing insights and recommendations for future research and benchmarking.

Contribution

It formally characterizes MIL problem types, analyzes their influence on algorithm performance, and offers guidance for future research and benchmarking efforts.

Findings

MIL problem characteristics significantly affect algorithm performance

Performance varies across different data distributions and ambiguity levels

Experiments highlight the importance of problem characteristics in MIL applications

Abstract

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. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.