Algorithm Selection Framework: A Holistic Approach to the Algorithm Selection Problem

The algorithm selection framework uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategyand generates a ranked list of recommended analysis techniques. In seven of nine sample problems, the recall of the top ranked recommendation was considered "good" with at least 90% of the best observed recall. Pareto efficientrecommendations for recall and run time were generated for three of the problems. The framework measured well against the pre-defined criteria. The framework successfully used information in the problem to recommend appropriate algorithms.