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Naslov:Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Avtorji:ID Lavrač, Nada, Jozef Stefan Institute and University of Nova Gorica (Avtor)
ID Fuernkranz, Johannes, Technische Universitaet Darmstat, Germany (Avtor)
ID Gamberger, Dragan, Rudjer Boskovic Institute, Croatia (Avtor)
Datoteke: Gradivo nima datotek, ki so prostodostopne za javnost. Gradivo je morda fizično dosegljivo v knjižnici fakultete, zalogo lahko preverite v COBISS-u. Povezava se odpre v novem oknu
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano
Tipologija:1.16 - Samostojni znanstveni sestavek ali poglavje v monografski publikaciji
Organizacija:UNG - Univerza v Novi Gorici
Opis: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.
Ključne besede:Machine learning, Feature construction, Rule learning, Unknown attribute values
Status publikacije:Objavljeno
Leto izida:2010
Št. strani:26
PID:20.500.12556/RUNG-3246 Novo okno
COBISS.SI-ID:4843771 Novo okno
DOI:10.1007/978-3-642-05177-7_6 Novo okno
NUK URN:URN:SI:UNG:REP:OI29IXGR
Datum objave v RUNG:14.07.2017
Število ogledov:5492
Število prenosov:0
Metapodatki:XML DC-XML DC-RDF
:
LAVRAČ, Nada, FUERNKRANZ, Johannes in GAMBERGER, Dragan, 2010, Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. V : Advances in Machine Learning I [na spletu]. Berlin Heidelberg. [Dostopano 7 april 2025]. Pridobljeno s: https://repozitorij.ung.si/IzpisGradiva.php?lang=slv&id=3172
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Gradivo je del monografije

Naslov:Advances in Machine Learning I : Dedicated to the memory of professor Ryszard S. Michalski
Uredniki:Jacek Koronacki, Zbigniew W Ras, Sĺawomir T. Wierzchoń, Janusz Kacprzyk
Kraj izida:Berlin Heidelberg
Založnik:Springer Verlag
Leto izida:2010
ISBN:978-3-642-05176-0

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