Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

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 (Maron, 1998; Maron & Lozano-P?erez, 1998) and of a method based on the chi-squared statistic (McGovern & Jensen, 2003). 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.

• Type: Conference
• Year: 2003
• Where: ICML
• Authors: Amy McGovern, David Jensen
• Keywords: ICML 2003, Illustrative Data Set, Machine Learning, Predictive Structures, Relational Data