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Enhancing Supervised Classifications with Metamorphic Relations

Conference Paper


Abstract


  • © 2018 Association for Computing Machinery. We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers (Artcodes). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing.

Authors


  •   Xu, Liming (external author)
  •   Towey, Dave (external author)
  •   French, Andrew (external author)
  •   Benford, Steve (external author)
  •   Zhi Quan (George) Zhou
  •   Chen, Tsong Yueh (external author)

Publication Date


  • 2018

Citation


  • Xu, L., Towey, D., French, A., Benford, S., Zhou, Z. & Chen, T. (2018). Enhancing Supervised Classifications with Metamorphic Relations. Proceedings - International Conference on Software Engineering (pp. 46-53). United States: ACM.

Scopus Eid


  • 2-s2.0-85051271168

Start Page


  • 46

End Page


  • 53

Place Of Publication


  • United States

Abstract


  • © 2018 Association for Computing Machinery. We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers (Artcodes). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing.

Authors


  •   Xu, Liming (external author)
  •   Towey, Dave (external author)
  •   French, Andrew (external author)
  •   Benford, Steve (external author)
  •   Zhi Quan (George) Zhou
  •   Chen, Tsong Yueh (external author)

Publication Date


  • 2018

Citation


  • Xu, L., Towey, D., French, A., Benford, S., Zhou, Z. & Chen, T. (2018). Enhancing Supervised Classifications with Metamorphic Relations. Proceedings - International Conference on Software Engineering (pp. 46-53). United States: ACM.

Scopus Eid


  • 2-s2.0-85051271168

Start Page


  • 46

End Page


  • 53

Place Of Publication


  • United States