Ensuring Dataset Quality for Machine Learning Certification

• URL: http://arxiv.org/abs/2011.01799v1
• Date: Tue, 3 Nov 2020 15:45:43 GMT
• Title: Ensuring Dataset Quality for Machine Learning Certification
• Authors: Sylvaine Picard, Camille Chapdelaine, Cyril Cappi, Laurent Gardes, Eric Jenn, Baptiste Lef\`evre, Thomas Soumarmon
• Abstract summary: We show that the specificities of the Machine Learning context are neither properly captured nor taken into ac-count. We propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain.
• Score: 0.6927055673104934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.

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