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Author: Kevin Swingler

Affiliation: University of Stirling, United Kingdom

Keyword(s): Data preparation, Machine learning, Data mining, Data quality, Quantity.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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 several formats:
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 (IJCCI 2011) - NCTA; ISBN 978-989-8425-84-3, SciTePress, pages 405-409. DOI: 10.5220/0003687104050409

@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 (IJCCI 2011) - NCTA},
year={2011},
pages={405-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003687104050409},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications (IJCCI 2011) - NCTA
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
PB - SciTePress

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