G.: Experiment databases: Creating a new platform for meta-learning research

Many studies in machine learning try to in-vestigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossi-ble for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how orga-nizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, al-lowing effective reuse of previous experimen-tation and targeted analysis of the collected results. 1