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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14118

Title: ACORA: Distribution-Based Aggregation for Relational Learning from Identifier Attributes
Authors: Perlich, Claudia
Provost, Foster
Issue Date: Feb-2005
Publisher: Stern School of Business, New York University
Series/Report no.: CeDER-04-04
Abstract: Feature construction through aggregation plays an essential role in modeling relational domains with one-to-many relationships between tables. One-to-many relationships lead to bags (multisets) of related entities, from which predictive information must be captured. This paper focuses on aggregation from categorical attributes that can take many values (e.g., object identifiers). We present a novel aggregation method as part of a relational learning system ACORA, that combines the use of vector distance and meta-data about the class-conditional distributions of attribute values. We provide a theoretical foundation for this approach deriving a "relational fixed-effect" model within a Bayesian framework, and discuss the implications of identifier aggregation on the expressive power of the induced model. One advantage of using identifier attributes is the circumvention of limitations caused either by missing/unobserved object properties or by independence assumptions. Finally, we show empirically that the novel aggregators can generalize in the presence of identi- fier (and other high-dimensional) attributes, and also explore the limitations of the applicability of the methods.
URI: http://hdl.handle.net/2451/14118
Appears in Collections:CeDER Working Papers
IOMS: Information Systems Working Papers

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