%0 Conference Proceedings
%T Using Ontologies to Express Prior Knowledge for Genetic Programming
%+ University of Linz - Johannes Kepler Universität Linz (JKU)
%+ RISC Software GmbH
%+ University of Applied Sciences Upper Austria (FH OÖ)
%A Prieschl, Stefan
%A Girardi, Dominic
%A Kronberger, Gabriel
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Canterbury, United Kingdom
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11713
%P 362-376
%8 2019-08-26
%D 2019
%R 10.1007/978-3-030-29726-8_23
%K Supervised learning
%K Ontologies
%K Domain knowledge
%K Genetic programming
%K Symbolic regression
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02520033/document
%2 https://inria.hal.science/hal-02520033/file/485369_1_En_23_Chapter.pdf
%L hal-02520033
%U https://inria.hal.science/hal-02520033
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11713