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The Focused Multi-Criteria Ranking Approach to Machine Learning Algorithm Selection-An Incremental Meta Learning Assistant for Data Mining Tasks

The main goal of the ESPRIT METAL project is to use metalearning to develop an incrementally adaptable assistant system to provide usersupport in machine learning and data mining. Meta data consists of performance outcomes of ML algorithms on known datasets. Using new models of data envelopment analysis to deal with multiple criteria, an ordered ranking of algorithms is obtained for each dataset. In order to suggest a specific machine learning solution for a given application, characteristics of datasets are compared. Meta knowledge of predictive reliability combined with a nearest neighbour approach provides a means for incremental learning that is focused on the most relevant information. The meta-knowledge base is extended dynamically and can hence adapt. User experiences can be taken into account by feedback of the quality of suggested solutions into the focusing process. Results from training and testing trials are presented and informational gains are evaluated.