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Authors: Jorge López ; Maxime Labonne and Claude Poletti

Affiliation: Airbus Defence and Space, Issy-Les-Moulineaux, France

Keyword(s): Machine Learning, Data Set Collection, Formal Verification, Trusted Artificial Intelligence.

Abstract: In order to properly train a machine learning model, data must be properly collected. To guarantee a proper data collection, verifying that the collected data set holds certain properties is a possible solution. For example, guaranteeing that that data set contains samples across the whole input space, or that the data set is balanced w.r.t. different classes. We present a formal approach for verifying a set of arbitrarily stated properties over a data set. The proposed approach relies on the transformation of the data set into a first order logic formula, which can be later verified w.r.t. the different properties also stated in the same logic. A prototype tool, which uses the z3 solver, has been developed; the prototype can take as an input a set of properties stated in a formal language and formally verify a given data set w.r.t. to the given set of properties. Preliminary experimental results show the feasibility and performance of the proposed approach, and furthermore the flexi bility for expressing properties of interest. (More)

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Paper citation in several formats:
López, J.; Labonne, M. and Poletti, C. (2021). Toward Formal Data Set Verification for Building Effective Machine Learning Models. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 249-256. DOI: 10.5220/0010676500003064

@conference{kdir21,
author={Jorge López. and Maxime Labonne. and Claude Poletti.},
title={Toward Formal Data Set Verification for Building Effective Machine Learning Models},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR},
year={2021},
pages={249-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010676500003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR
TI - Toward Formal Data Set Verification for Building Effective Machine Learning Models
SN - 978-989-758-533-3
IS - 2184-3228
AU - López, J.
AU - Labonne, M.
AU - Poletti, C.
PY - 2021
SP - 249
EP - 256
DO - 10.5220/0010676500003064
PB - SciTePress

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