A novel nonlinear discriminant classifier trained by an evolutionary algorithm

Ziauddin Ursani, David W. Corne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


In recent work, we developed a classifier inspired by reliability engineering, in which the boundaries between classes was constrained based on parameterized formulae involving single feature probabilities. This produced competitive results on the 'Iris Flower' dataset, however it was not suitable for learning tasks with highly nonlinear class boundaries (where class membership correlates very poorly with any individual feature value). The 'Balance Scale' dataset is an example of the latter type of dataset, where individual features can only influence collectively on the decision of class membership. Keeping this in mind, in this paper we describe a new nonlinear discriminant classifier, in which an evolutionary algorithm learns a probabilistic model based on a constrained, parameterized combination of all feature values. We test this method on both the 'Iris Flower' and 'Balance Scale' datasets. The results are highly competitive.

Original languageEnglish
Title of host publicationProceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450363532
Publication statusPublished - 26 Feb 2018


  • Classification
  • Machine learning
  • Supervised learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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