Accession Number:

ADA465314

Title:

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

Descriptive Note:

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2003-01-01

Pagination or Media Count:

9.0

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.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE