Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Top-down induction of first-order logical decision trees
Artificial Intelligence
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Multi-relational classification is an important data mining task, since much real world data is organized in multiple relations. The major challenges come from, firstly, the large high dimensional search spaces due to many attributes in multiple relations and, secondly, the high computational cost in feature selection and classifier construction due to the high complexity in the structure of multiple relations. The existing approaches mainly use the inductive logic programming (ILP) techniques to derive hypotheses or extract features for classification. However, those methods often are slow and sometimes cannot provide enough information to build effective classifiers. In this paper, we develop a general approach for accurate and fast multi-relational classification using feature generation and selection. Moreover, we propose a novel similarity-based feature selection method for multi-relational classification. An extensive performance study on several benchmark data sets indicates that our approach is accurate, fast and highly scalable.