C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Transformation-Based Learning Using Multirelational Aggregation
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
The Knowledge Engineering Review
Speeding up and boosting diverse density learning
DS'10 Proceedings of the 13th international conference on Discovery science
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Following the success of inductive logic programming on structurally complex but small problems, recently there has been strong interest in relational methods that scale to real-world databases. Propositionalization has already been shown to be a particularly promising approach for robustly and effectively handling larger relational data sets. However, the number of propositional features generated here tends to quickly increase, e.g. with the number of relations, with negative effects especially for the efficiency of learning. In this paper, we show that feature selection techniques can significantly increase the efficiency of transformation-based learning without sacrificing accuracy.