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Authors: Andrea Murari 1 ; Emmanuele Peluso 2 ; Saeed Talebzadeh 2 ; Pasqualino Gaudio 2 ; Michele Lungaroni 2 ; Ondrej Mikulin 3 ; Jesus Vega 4 and Michela Gelfusa 2

Affiliations: 1 Consorzio RFX (CNR, ENEA, INFN, Università di Padova and Acciaierie Venete SpA), Italy ; 2 University of Rome “Tor Vergata”, Italy ; 3 Institute of Plasma Physics AS CR, Czech Republic ; 4 Asociación EURATOM/CIEMAT para Fusión, Spain

ISBN: 978-989-758-271-4

Keyword(s): Machine Learning Tools, Support Vector Machines, Symbolic Regression, Genetic Programming.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: In many scientific applications, it is necessary to perform classification, which means discrimination between examples belonging to different classes. Machine Learning Tools have proved to be very performing in this task and can achieve very high success rates. On the other hand, the “realism” and interpretability of their results are very low, limiting their applicability. In this paper, a method to derive manageable equations for the hypersurface between classes is presented. The main objective consists of formulating the results of machine learning tools in a way representing the actual “physics” behind the phenomena under investigation. The proposed approach is based on a suitable combination of Support vector Machines and Symbolic Regression via Genetic Programming; it has been investigated with a series of systematic numerical tests, for different types of equations and classification problems, and tested with various experimental databases. The obtained results indicate that t he proposed method permits to find a good trade-off between accuracy of the classification and complexity of the derived mathematical equations. Moreover, the derived models can be tuned to reflect the actual phenomena, providing a very useful tool to bridge the gap between data, machine learning tools and scientific theories. (More)

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Paper citation in several formats:
Murari A., Peluso E., Talebzadeh S., Gaudio P., Lungaroni M., Mikulin O., Vega J. and Gelfusa M. (2017). Deriving Realistic Mathematical Models from Support Vector Machines for Scientific Applications.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 102-113. DOI: 10.5220/0006517401020113

@conference{kdir17,
author={Andrea Murari and Emmanuele Peluso and Saeed Talebzadeh and Pasqualino Gaudio and Michele Lungaroni and Ondrej Mikulin and Jesus Vega and Michela Gelfusa},
title={Deriving Realistic Mathematical Models from Support Vector Machines for Scientific Applications},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={102-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006517401020113},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Deriving Realistic Mathematical Models from Support Vector Machines for Scientific Applications
SN - 978-989-758-271-4
AU - Murari A.
AU - Peluso E.
AU - Talebzadeh S.
AU - Gaudio P.
AU - Lungaroni M.
AU - Mikulin O.
AU - Vega J.
AU - Gelfusa M.
PY - 2017
SP - 102
EP - 113
DO - 10.5220/0006517401020113

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