Multiple Instance Learning With Multiple Positive And Negative Target Concepts

We introduce a new algorithm that maps multiple instance data using both positive and negative target concepts into a data representation suitable for standard classication. Multiple instance data are characterized by bags which are in turn characterized by a variable number of feature vectors or instances. Each bag has a known positive or negative label, but the labels of any given instances within a bag is unknown. First, we use the Fuzzy Clustering of Multiple Instance data (FCMI) algorithm to identify K+ positive target concepts, which represent points in the feature space that are close to instances from positive bags, and distant to instances from negative bags. We use a simple K-means clustering algorithm to identify K negative target concepts that supplement the positive target concepts. Next we demonstrate how the positive and negative target concepts can be used to embed each bag, which has a variable number of instances, into a feature vector with xed dimension. A key advantage to embedded instance space feature vectors is that standard machine learning algorithms may be used in training and testing multiple instance data. Another advantage of our embedding is that it provides a simple and intuitive interpretation of the data. We show that using our feature embedding, coupled with standard classiers such as support vector machines or k-nearest neighbors, can outperform state-of- the-art Multiple Instance Learning classiers on benchmark datasets.