Ensuring Dataset Quality for Machine Learning Certification
- 1 October 2020
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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 account. 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 addition, 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.Keywords
This publication has 4 references indexed in Scilit:
- Formal Implementation of Data Validation for Railway Safety-Related Systems with OVADOLecture Notes in Computer Science, 2014
- Data sets and data quality in software engineeringPublished by Association for Computing Machinery (ACM) ,2008
- Empirical Bernstein stoppingPublished by Association for Computing Machinery (ACM) ,2008
- Data quality requirements analysis and modelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002