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Title:Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Authors:Lavrač, Nada (Author)
Fuernkranz, Johannes (Author)
Gamberger, Dragan (Author)
Files:This document has no files. This document may have a phisical copy in the library of the organization, check the status via COBISS. Link is opened in a new window
Language:English
Work type:Not categorized (r6)
Tipology:1.16 - Independent Scientific Component Part or a Chapter in a Monograph
Organization:UNG - University of Nova Gorica
Abstract:Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Keywords:Machine learning, Feature construction, Rule learning, Unknown attribute values
Year of publishing:2010
Number of pages:26
COBISS_ID:4843771 Link is opened in a new window
URN:URN:SI:UNG:REP:OI29IXGR
DOI:10.1007/978-3-642-05177-7_6 Link is opened in a new window
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Record is a part of a monograph

Title:Advances in Machine Learning I
Subtitle:Dedicated to the memory of professor Ryszard S. Michalski
Publisher:Springer Verlag
ISBN:978-3-642-05176-0
Place of publishing:Berlin Heidelberg
Year of publishing:2010
Editors:Jacek Koronacki, Zbigniew W Ras, Sĺawomir T. Wierzchoń, Janusz Kacprzyk

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