Relational Data Mining
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Community-Based Platform for Machine Learning Experimentation
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Towards an Ontology of Data Mining Investigations
DS '09 Proceedings of the 12th International Conference on Discovery Science
An integrated multi-task inductive database VINLEN: initial implementation and early results
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Selecting classification algorithms with active testing
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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Data mining and machine learning are experimental sciences: a lot of insight in the behaviour of algorithms is obtained by implementing them and studying how they behave when run on datasets. However, such experiments are often not as extensive and systematic as they ideally would be, and therefore the experimental results must be interpreted with caution. In this paper we present a new experimental methodology that is based on the concept of “experiment databases”. An experiment database can be seen as a special kind of inductive database, and the experimental methodology consists of filling and then querying this database. We show that the novel methodology has numerous advantages over the existing one. As such, this paper presents a novel and interesting application of inductive databases that may have a significant impact on experimental research in machine learning and data mining.