Interpretable machine learning with reject option

  • 1 CITEC Center of Excellence, Bielefeld University, Bielefeld, Germany
Johannes Brinkrolf
  • CITEC Center of Excellence, Bielefeld University, Bielefeld, Germany
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  • Johannes Brinkrolf received his master’s degree from Bielefeld University in 2016. From April 2016 to September 2016 he was a research assistant at the South Westphalia University of Applied Science. Since October 2016 he has been a PhD student at the Cognitive Interaction Technology Center of Excellence at Bielefeld University.
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and Barbara Hammer
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  • CITEC Center of Excellence, Bielefeld University, Bielefeld, Germany
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  • Barbara Hammer received her Ph.D. in Computer Science in 1999 and her venia legendi in Computer Science in 2003, both from the University of Osnabrueck, Germany. From 2000-2004, she was chair of the junior research group ‘Learning with Neural Methods on Structured Data’ at the University of Osnabrueck before accepting an offer as professor for Theoretical Computer Science at Clausthal University of Technology, Germany, in 2004. Since 2010, she holds a professorship for Theoretical Computer Science for Cognitive Systems at the CITEC cluster of excellence at Bielefeld University, Germany. Several research stays have taken her to Italy, the U.K., India, France, the Netherlands, and the U.S.A. Her areas of expertise include hybrid systems, self-organizing maps, clustering, and recurrent networks as well as applications in bioinformatics, industrial process monitoring, or cognitive science. She chaired the IEEE CIS Technical Committee on Data Mining in 2013/2014, and she is chair of the Fachgruppe Neural Networks of the GI and vice-chair of the GNNs. She has published more than 200 contributions to international conferences / journals, and she is coauthor/editor of four books.
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Abstract

Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples. In this contribution, we give an overview about a popular alternative technology from machine learning, namely modern variants of learning vector quantization, which, due to their combined discriminative and generative nature, incorporate interpretability and the possibility of explicit reject options for irregular samples. We give an explicit bound on minimum changes required for a change of the classification in case of LVQ networks with reject option, and we demonstrate the efficiency of reject options in two examples.

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