Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

Proceedings of the Twentieth International Conference on Machine Learning

Volume

Proceedings of the Twentieth International Conference on Machine Learning

Contents

This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure that can explain a given labeling of the data and can predict labels of unseen data. Multiple-instance learning has previously only been applied to flat, or propositional, data and we present a modification to the framework that allows multiple-instance techniques to be used on relational data. We present experimental results using a relational modification of the diverse density method and of a method based on the chi-squared statistic. We demonstrate that multipleinstance learning can be used to identify predictive structures on both a small illustrative data set and the Internet Movie Database. We compare the classification results to a k-nearest neighbor approach.