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dc.contributor.authorSalisu Mamman Abdulrhamanen
dc.contributor.authorPavel Brazdilen
dc.description.abstractThe vast majority of studies in meta-learning uses only few performance measures when characterizing different machine learning algorithms. The measure Adjusted Ratios of Ratio (ARR) addresses the problem of how to evaluate the quality of a model based on the accuracy and training time. Unfortunately, this measure suffers from a shortcoming that is described in this paper. A new solution is proposed and it is shown that the proposed function satisfies the criterion of monotonicity, unlike ARR.en
dc.titleMeasures for Combining Accuracy and Time for Meta-learningen
Appears in Collections:LIAAD - Articles in International Conferences

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