Future directions in Multiple Instance Learning
David Chiu, Iker Gondra, Tao Xu
D. Chiu, I. Gondra, T. Xu, "Future directions in Multiple Instance Learning", Journal of Theoretical and Applied Computer Science, vol. 7, no. 3, pp. 29-39, 2013.
machine learning, pattern recognition, multiple instance learning, total entropy, partial entropy
In Multiple Instance Learning, each training sample consists of a set of unlabelled instances. The set as a whole is labeled positive if at least one instance in the set is positive, or negative otherwise. Given such training samples, the goal is to learn either an explicit description of the common positive instance(s) or a bag classifier that can assign labels to bags. Previous research has focused on this standard definition of the problem where instances in a set are independent. This raises a question: if we remove the independence assumption, can we generalize the goal of finding a description of the common instance(s) to that of finding a description of the common pattern(s) among instances? Similarly, can we generate bag classifiers that discriminate based on common pattern(s) among instances instead of just common instance(s)? This question raises many other related questions that have not been yet fully explored in the context of this problem. In this paper we first present a survey of existing methods that work with the standard definition of the problem and then elaborate on the previous question in the hope that researchers will investigate this exciting research direction.