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Authors: Terence Fusco ; Yaxin Bi ; Haiying Wang and Fiona Browne

Affiliation: Computer Science Research Institute, Ulster University, Shore Road, Newtownabbey, Antrim and Northern Ireland

ISBN: 978-989-758-318-6

Keyword(s): Optimisation, Over-sampling, Schistosomiasis, Synthetic Instance Generation, SMOTE, SMAC, SIMO.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Predictive Modeling ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In this paper, research is presented for improving optimisation performance using sparse training data for disease vector classification. Optimisation techniques currently available such as Bayesian, Evolutionary and Global optimisation and are capable of providing highly efficient and accurate results however, performance potential can often be restricted when dealing with limited training resources. In this study, a novel approach is proposed to address this issue by introducing Sequential Model-based Algorithm Configuration(SMAC) optimisation in combination with Synthetic Minority Over-sampling Technique(SMOTE) for optimised synthetic prediction modelling. This approach generates additional synthetic instances from a limited training sample while concurrently seeking to improve best algorithm performance. As results show, the proposed Synthetic Instance Model Optimisation (SIMO) technique presents a viable, unified solution for finding optimum classifier performance when faced with sparse training resources. Using the SIMO approach, noticeable performance accuracy and f-measure improvements were achieved over standalone SMAC optimisation. Many results showed significant improvement when comparing collective training data with SIMO instance optimisation including individual performance accuracy increases of up to 46% and a mean overall increase for the entire 240 configurations of 13.96% over standard SMAC optimisation. (More)

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Paper citation in several formats:
Fusco, T.; Bi, Y.; Wang, H. and Browne, F. (2018). Synthetic Optimisation Techniques for Epidemic Disease Prediction Modelling.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 95-106. DOI: 10.5220/0006823800950106

@conference{data18,
author={Terence Fusco. and Yaxin Bi. and Haiying Wang. and Fiona Browne.},
title={Synthetic Optimisation Techniques for Epidemic Disease Prediction Modelling},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={95-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006823800950106},
isbn={978-989-758-318-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Synthetic Optimisation Techniques for Epidemic Disease Prediction Modelling
SN - 978-989-758-318-6
AU - Fusco, T.
AU - Bi, Y.
AU - Wang, H.
AU - Browne, F.
PY - 2018
SP - 95
EP - 106
DO - 10.5220/0006823800950106

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