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KDD
2003
ACM

Learning relational probability trees

10 years 3 months ago
Learning relational probability trees
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 ...
Jennifer Neville, David Jensen, Lisa Friedland, Mi
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2003
Where KDD
Authors Jennifer Neville, David Jensen, Lisa Friedland, Michael Hay
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