Distribution-based aggregation for relational learning with identifier attributes

Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The aggregations used by existing methods are simple summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM,

• Type: Journal
• Year: 2006
• Where: ML
• Authors: Claudia Perlich, Foster J. Provost
• Keywords: Abstract Identifier, Categorical Attributes, Machine Learning, ML 2006, Particular Set