Knowledge discovery through symbolic regression with HeuristicLab

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4 Citations (Scopus)

Abstract

This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages824-827
Number of pages4
Volume7524
EditionPART 2
DOIs
Publication statusPublished - 2012
Event2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom
Duration: 24 Sep 201228 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7524 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
CountryUnited Kingdom
CityBristol
Period24.09.201228.09.2012

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