EASD - Experimental Algorithmics for Streaming Data

This paper proposes an experimental methodology for on-line machine learning algorithms, i.e., for algorithms that work on data that are available in a sequential order. It is demonstrated how established tools from experimental algorithmics (EA) can be applied in the on-line or streaming data setting. The massive on-line analysis (MOA) framework is used to perform the experiments. Benefits of a well-defined report structure are discussed. The application of methods from the EA community to on-line or streaming data is referred to as experimental algorithmics for streaming data (EADS)