Advanced Oversampling for Improved Detection of Software Anomalies in a Robot

• Nuño Basurto ^[1] ; Michał Woźniak ^[2] ; Carlos Cambra ^[1] ; Álvaro Herrero ^[1]
  1. [1] Universidad de Burgos

     Universidad de Burgos

     Burgos, España

     
  2. [2] Wroclaw University of Science and Technology. Department of Systems and Computer Networks. Wybrzeze Wyspianskiego 27, 50-370 (Poland)
• Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío , Carlos Cambra Baseca , Daniel Urda Muñoz , Javier Sedano Franco , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-57802-2, págs. 3-12
• Idioma: inglés
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• Resumen

  • Anomaly detection has been a challenging topic for decades and it still is open to new contributions nowadays. More specifically, the detection of anomalies (not only hardware ones but also those affecting the software) suffers from many problems when monitoring cyberphysical systems. One such usual problem is the much fewer data samples of anomalies than those available for the normal functioning of systems. This class-imbalance problem is addressed in the present paper and a novel strategy for oversampling the minority class is applied to an open dataset containing information about the performance of a component-based robot. The proposed strategy mainly consists on selecting the instances to be oversampled according to different criteria instead of randomly oversampling. Obtained results demonstrate that the proposed strategy improves predictive performance, especially when the SVM (Support Vector Machine) is used as classifier.