Readable and accurate rulesets with ORGA

M. N R Daud, David Corne

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


A key task for data mining is to produce accurate and descriptive models. 'Human readable' models are often necessary to enable understanding, potentially leading to further insight, and also inducing trust in the user. Rules, or decision trees (if not too numerous or large) are readable, unlike, for example SVM models. However, descriptiveness and accuracy normally conflict; a challenge is to find algorithms that have both high accuracy and high readability. We introduce ORGA (Optimized Ripper using Genetic Algorithm) which hybridizes evolutionary search with the RIPPER ruleset algorithm. RIPPER is effective at producing accurate and readable rulesets, and we show that ORGA provides significant further improvement. ORGA outperforms overall a suitable set of comparative algorithms including implementations of RIPPER, C4.5 and PART. On a majority of the datasets, ORGA's outperformance of the other algorithms is spectacular, and it is rarely dominated in terms of both accuracy and readability. © 2008 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN X - 10th International Conference, Proceedings
Number of pages10
Volume5199 LNCS
Publication statusPublished - 2008
Event10th International Conference on Parallel Problem Solving from Nature - Dortmund, Germany
Duration: 13 Sept 200817 Sept 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5199 LNCS
ISSN (Print)0302-9743


Conference10th International Conference on Parallel Problem Solving from Nature
Abbreviated titlePPSN X


  • Data mining
  • Human readability
  • Hybrid machine learning


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