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Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Johannes Fuernkranz, Nada Lavrač, Dragan Gamberger, 2010, samostojni znanstveni sestavek ali poglavje v monografski publikaciji

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.
Najdeno v: ključnih besedah
Ključne besede: Machine learning, Feature construction, Rule learning, Unknown attribute values
Objavljeno: 14.07.2017; Ogledov: 272; Prenosov: 0
.pdf Polno besedilo (365,76 KB)

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