Hinojosa Herrera, Ana Elsa ORCID: https://orcid.org/0000-0002-0636-1881, Walshaw, Chris ORCID: https://orcid.org/0000-0003-0253-7779 and Bailey, Chris ORCID: https://orcid.org/0000-0002-9438-3879 (2020) Improving Black Box Classification Model Veracity for Electronics Anomaly Detection. In: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp. 1092-1097. ISBN 978-1728151694 ISSN 2156-2318 (Print), 2158-2297 (Online) (doi:10.1109/ICIEA48937.2020.9248258)

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Abstract

Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models.

Item Type:	Conference Proceedings
Title of Proceedings:	2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)
Additional Information:	Conference held from 9-13 Nov. 2020 at Kristiansand, Norway.
Uncontrolled Keywords:	Black Box, Classification, Veracity, Feature Selection, KNN, Stepwise
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) Faculty of Engineering & Science > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:06
URI:	http://gala.gre.ac.uk/id/eprint/32292

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