2001 PropositionalizationApproachest

• (Kramer et al., 2001) ⇒ Stefan Kramer, Nada Lavrač, and Peter Flach. (2001). “Propositionalization Approaches to Relational Data Mining.” In: Relational Data Mining. ISBN:3-540-42289-7

Subject Headings: Propositionalization.

Cited By

• http://scholar.google.com/scholar?q=%222001%22+Propositionalization+Approaches+to+Relational+Data+Mining
• http://dl.acm.org/citation.cfm?id=567222.567236&preflayout=flat#citedby

Quotes

Abstract

This chapter surveys methods that transform a relational representation of a learning problem into a propositional (feature-based, attribute-value) representation. This kind of representation change is known as propositionalization. Taking such an approach, feature construction can be decoupled from model construction. It has been shown that in many relational data mining applications this can be done without loss of predictive performance. After reviewing both general-purpose and domain-dependent propositionalization approaches from the literature, an extension to the LINUS propositionalization method that overcomes the system's earlier inability to deal with non-determinate local variables is described.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 PropositionalizationApproachest	Stefan Kramer Peter A. Flach Nada Lavrač			Propositionalization Approaches to Relational Data Mining						2001