Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
Abstract
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 multiple instance 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 kappa-nearest neighbor approach.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2003
- Accession Number
- ADA465314
Entities
People
- Amy Mcgovern
- David Jensen
Organizations
- University of Massachusetts Amherst