2003 AggregationbasedFeatureInventio

• (Perlich & Provost, 2003) ⇒ Claudia Perlich, and Foster Provost. (2003). “Aggregation-based Feature Invention and Relational Concept Classes.” In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge discovery and data mining. ISBN:1-58113-737-0 doi:10.1145/956750.956772

Subject Headings: Propositionalization.

Cited By

• http://scholar.google.com/scholar?q=%222003%22+Aggregation-based+Feature+Invention+and+Relational+Concept+Classes
• http://dl.acm.org/citation.cfm?id=956750.956772&preflayout=flat#citedby

Quotes

Abstract

Model induction from relational data requires aggregation of the values of attributes of related entities. This paper makes three contributions to the study of relational learning. (1) It presents a hierarchy of relational concepts of increasing complexity, using relational schema characteristics such as cardinality, and derives classes of aggregation operators that are needed to learn these concepts. (2) Expanding one level of the hierarchy, it introduces new aggregation operators that model the distributions of the values to be aggregated and (for classification problems) the differences in these distributions by class. (3) It demonstrates empirically on a noisy business domain that more-complex aggregation methods can increase generalization performance. Constructing features using target-dependent aggregations can transform relational prediction tasks so that well-understood feature-vector-based modeling algorithms can be applied successfully.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 AggregationbasedFeatureInventio	Foster Provost Claudia Perlich			Aggregation-based Feature Invention and Relational Concept Classes				10.1145/956750.956772		2003