Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning and Its Applications, Advanced Lectures
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Experiment databases: a novel methodology for experimental research
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Learning from the Past with Experiment Databases
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
UCI++: Improved Support for Algorithm Selection Using Datasetoids
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
TunedIT.org: system for automated evaluation of algorithms in repeatable experiments
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Auto-experimentation of KDD workflows based on ontological planning
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Combining meta-learning and active selection of datasetoids for algorithm selection
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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Machine learning research often has a large experimental component. While the experimental methodology employed in machine learning has improved much over the years, repeatability of experiments and generalizability of results remain a concern. In this paper we propose a methodology based on the use of experiment databases. Experiment databases facilitate large-scale experimentation, guarantee repeatability of experiments, improve reusability of experiments, help explicitating the conditions under which certain results are valid, and support quick hypothesis testing as well as hypothesis generation. We show that they have the potential to significantly increase the ease with which new results in machine learning can be obtained and correctly interpreted.