A Strategy for Ranking Optimization Methods using Multiple Criteria

An important component of a suitably automated machine learning process is the automation of the model selection which often contains some optimal selection of hyperparameters. The hyperparameter optimization process is often conducted with a black-box tool, but, because different tools may perform better in different circumstances, automating the machine learning workflow might involve choosing the appropriate optimization method for a given situation. This paper proposes a mechanism for comparing the performance of multiple optimization methods for multiple performance metrics across a range of optimization problems. Using nonparametric statistical tests to convert the metrics recorded for each problem into a partial ranking of optimization methods, results from each problem are then amalgamated through a voting mechanism to generate a final score for each optimization method. Mathematical analysis is provided to motivate decisions within this strategy, and sample results are provided to demonstrate the impact of certain ranking decisions.