The Perils of Ignoring Data Suitability: The Suitability of Data Used to Train Neural Networks Deserves More Attention

Abstract

The quality and quantity (we call it suitability from now on) of data that are used for a machine learning task are as important as the capability of the machine learning algorithm itself. Yet these two aspects of machine learning are not given equal weight by the data mining, machine learning and neural computing communities. Data suitability is largely ignored compared to the effort expended on learning algorithm development. This position paper argues that some of the new algorithms and many of the tweaks to existing algorithms would be unnecessary if the data going into them were properly pre-processed, and calls for a shift in effort towards data suitability assessment and correction.