A hierarchical set-partitioning nonlinear discriminant classifier trained by an evolutionary algorithm

Ziauddin Ursani, David W. Come

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

1 Citation (Scopus)

Abstract

This paper is the fourth in our series of papers on a novel nonlinear discriminant classifier. The model is based on elementary mathematical operators, yet is able to produce 100% accuracy on training data without compromising generalization ability. The reason behind the model's capabilities stem from its ability to partition the training data into a hierarchy of groups in such a way that a separate model is trained for each subgroup. Therefore, a Hierarchical Set-partitioning Nonlinear Discriminant Classifier is essentially a hierarchical arrangement of various models corresponding to hierarchical arrangement of various partitions/subgroups. In the latest version described here, the model has a low number of parameters, and we have extended its set-partitioning approach to include the concept of remainder unclassified group. These modifications have led to 100% results on the training set and highly competitive results on test sets when compared with the state of art on four popular test cases taken from the UCI repository.

Original languageEnglish
Title of host publication2018 International Conference on Artificial Intelligence and Big Data (ICAIBD)
PublisherIEEE
Pages15-20
Number of pages6
ISBN (Electronic)9781538669877
DOIs
Publication statusPublished - 28 Jun 2018

Keywords

  • Classification
  • Machine learning
  • Nonlinear discriminant classifier
  • Supervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management

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