GEML: Evolutionary unsupervised and semi-supervised learning of multi-class classification with grammatical evolution

Jeannie M. Fitzgerald, R. Muhammad Atif Azad, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With minor adaptations to the objective function the system can be trivially modified to work with the conceptually different paradigms of supervised, semi-supervised and unsupervised learning. The framework generates human readable solutions which explain the mechanics behind the classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. GEML is studied on a range of multi-class classification problems and is shown to be competitive with several state of the art multi-class classification algorithms.

Original languageEnglish
Title of host publicationECTA
EditorsAgostinho Rosa, Juan Julian Merelo, Antonio Dourado, Jose M. Cadenas, Kurosh Madani, Antonio Ruano, Joaquim Filipe, Joaquim Filipe
PublisherSciTePress
Pages83-94
Number of pages12
ISBN (Electronic)9789897581571
DOIs
Publication statusPublished - 2015
Event7th International Joint Conference on Computational Intelligence, IJCCI 2015 - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015

Publication series

NameIJCCI 2015 - Proceedings of the 7th International Joint Conference on Computational Intelligence
Volume1

Conference

Conference7th International Joint Conference on Computational Intelligence, IJCCI 2015
Country/TerritoryPortugal
CityLisbon
Period12/11/1514/11/15

Keywords

  • Evolutionary Computation
  • Grammatical Evolution
  • Machine Learning
  • Multi-class Classification
  • Semi-supervised Learning
  • Unsupervised Learning

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