Proceedings of the 1st International Workshop on Constraint-Based Mining and Learning pages:21-33
International Workshop on Constraint-Based Mining and Learning location:Warsaw, Poland date:September 21, 2007
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.