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Improving Black Box Classification Model Veracity for Electronics Anomaly Detection

Improving Black Box Classification Model Veracity for Electronics Anomaly Detection

Hinojosa Herrera, Ana Elsa ORCID: 0000-0002-0636-1881, Walshaw, Chris ORCID: 0000-0003-0253-7779 and Bailey, Chris ORCID: 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:https://doi.org/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 / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Numerical Modelling & Process Analysis (CNMPA)
Faculty of Liberal Arts & Sciences > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Mechanics & Reliability Group (CMRG)
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 11 May 2021 17:56
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32292

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