Multi-instance learning is a setting in supervised learning where the data consist of bags of instances. Samples in the dataset are groups of individual instances. In classification problems, a decision value is assigned to the entire bag, and the classification of an unseen bag involves the prediction of the decision value based on the instances it contains. In this paper, we develop a framework for multi-instance classifiers based on fuzzy set theory. Fuzzy sets have been used in many machine learning applications, but so far not in the classification of multi-instance data. We explore its untapped potential here. We interpret the classes as fuzzy sets and determine membership degrees of unseen bags to these sets based on the available training data. In doing so, we develop a framework of classifiers that extract the required membership degrees either at the level of instances (instance-based) or at the level of bags (bag-based). We offer an extensive analysis of the different settings within the proposed framework. We experimentally compare our proposal to state-of-the-art multi-instance classifiers, and based on two evaluation measures, our methods are shown to perform very well.
['Sarah Vluymans', 'Dánel Sánchez Tarragó', 'Yvan Saeys', 'Chris Cornelis', 'Francisco Herrera']
Fuzzy Multi-Instance Classifiers