  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.
