Effect of Incomplete Meta-dataset on Average Ranking Method
This work addresses the issue of computational efficiency in metalearning for researchers by showing that fewer tests may be acceptable, but it is incremental as it builds on an existing method.
The paper tackled the problem of how incomplete metadata affects the average ranking method in metalearning, and found that their upgraded method is relatively robust to omissions in test results.
One of the simplest metalearning methods is the average ranking method. This method uses metadata in the form of test results of a given set of algorithms on given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. We investigate the problem of how the process of generating the average ranking is affected by incomplete metadata including fewer test results. This issue is relevant, because if we could show that incomplete metadata does not affect the final results much, we could explore it in future design. We could simply conduct fewer tests and save thus computation time. In this paper we describe an upgraded average ranking method that is capable of dealing with incomplete metadata. Our results show that the proposed method is relatively robust to omission in test results in the meta datasets.