THE PERILS OF IGNORING DATA SUITABILITY - The Suitability of Data used to Train Neural Networks Deserves More Attention

Kevin Swingler

2011

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.

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Paper Citation


in Harvard Style

Swingler K. (2011). THE PERILS OF IGNORING DATA SUITABILITY - The Suitability of Data used to Train Neural Networks Deserves More Attention . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 405-409. DOI: 10.5220/0003687104050409


in Bibtex Style

@conference{ncta11,
author={Kevin Swingler},
title={THE PERILS OF IGNORING DATA SUITABILITY - The Suitability of Data used to Train Neural Networks Deserves More Attention},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={405-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003687104050409},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - THE PERILS OF IGNORING DATA SUITABILITY - The Suitability of Data used to Train Neural Networks Deserves More Attention
SN - 978-989-8425-84-3
AU - Swingler K.
PY - 2011
SP - 405
EP - 409
DO - 10.5220/0003687104050409