Using Ontologies to Express Prior Knowledge for Genetic Programming

Stefan Prieschl, Dominic Girardi, Gabriel Kronberger

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

1 Citation (Scopus)

Abstract

Ontologies are useful for modeling domains and can be used to capture expert knowledge about a system. Genetic programming can be used to identify statistical relationships or models from data. Combining expert knowledge as well as statistical rules identified solely from data is necessary in application domains where data is scarce and a large body of expert knowledge exists. We therefore study if the performance of genetic programming can be improved by incorporating prior knowledge from an ontology. In particular, we include prior knowledge as additional features for genetic programming. The approach is tested with six benchmark data sets where we compare the required computational effort that is necessary to find an acceptable model with and without additional features. The results show that additional features gathered from an ontology improve the performance of tree-based GP. The probability to find acceptable solutions with a fixed computational budget is increased. For noisy data sets we observed the same effect as for the data sets without noise.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction - Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Proceedings
EditorsAndreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl
PublisherSpringer Verlag
Pages362-376
Number of pages15
ISBN (Print)9783030297251
DOIs
Publication statusPublished - 2019
Event3rd IFIP Cross Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019 - Canterbury, United Kingdom
Duration: 26 Aug 201929 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11713 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd IFIP Cross Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019
CountryUnited Kingdom
CityCanterbury
Period26.08.201929.08.2019

Keywords

  • Domain knowledge
  • Genetic programming
  • Ontologies
  • Supervised learning
  • Symbolic regression

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