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|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status: ||Refereed|
|Author(s): ||Swingler, Kevin|
|Contact Email: ||firstname.lastname@example.org|
|Title: ||The Perils of Ignoring Data Suitability: The Suitability of Data Used to Train Neural Networks Deserves More Attention|
|Citation: ||Swingler K (2011) The Perils of Ignoring Data Suitability: The Suitability of Data Used to Train Neural Networks Deserves More Attention In: NCTA 2011 - International Conference on Neural Computation Theory and Application, SciTePress Digital Library. International Conference on Neural Computation Theory and Application, 24.10.2011 - 26.10.2011, Paris, France.|
|Issue Date: ||2011|
|Conference Name: ||International Conference on Neural Computation Theory and Application|
|Conference Dates: ||2011-10-24T00:00:00Z|
|Conference Location: ||Paris, France|
|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.|
|Status: ||Author version|
|Rights: ||Permission granted for use in this repository by The Institute for Systems and Technologies of Information, Control and Communication (INSTICC): http://www.insticc.org/InsticcPortal/Home.aspx|
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