NDLI: Learning relational probability trees

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Learning relational probability trees

Content Provider	ACM Digital Library
Author	Jensen, David Friedland, Lisa Hay, Michael Neville, Jennifer
Abstract	Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probability estimation trees. Traditional tree learning algorithms assume that instances in the training data are homogenous and independently distributed. Relational probability trees (RPTs) extend standard probability estimation trees to a relational setting in which data instances are heterogeneous and interdependent. Our algorithm for learning the structure and parameters of an RPT searches over a space of relational features that use aggregation functions (e.g. AVERAGE, MODE, COUNT) to dynamically propositionalize relational data and create binary splits within the RPT. Previous work has identified a number of statistical biases due to characteristics of relational data such as autocorrelation and degree disparity. The RPT algorithm uses a novel form of randomization test to adjust for these biases. On a variety of relational learning tasks, RPTs built using randomization tests are significantly smaller than other models and achieve equivalent, or better, performance.
Starting Page	625
Ending Page	630
Page Count	6
File Format	PDF
ISBN	1581137370
DOI	10.1145/956750.956830
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-08-24
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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