The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{DBLP:conf/flairs/JohanssonKN04,
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author = "Ulf Johansson and Rikard Konig and Lars Niklasson",
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title = "The Truth is In There - Rule Extraction from Opaque
Models Using Genetic Programming",
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booktitle = "Proceedings of the Seventeenth International Florida
Artificial Intelligence Research Society Conference",
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year = "2004",
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editor = "Valerie Barr and Zdravko Markov",
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pages = "658--663",
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address = "Miami Beach, Florida, USA",
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month = may # " 12-14",
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publisher = "AAAI Press",
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keywords = "genetic algorithms, genetic programming, G-REX,
symbolic regression trees, decision trees, fuzzy rules,
crisp rules",
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ISBN = "1-57735-201-7",
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URL = "https://dblp.org/db/conf/flairs/flairs2004.html",
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URL = "http://www.aaai.org/Papers/FLAIRS/2004/Flairs04-113.pdf",
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broken = "https://www.aaai.org/Library/FLAIRS/2004/flairs04-113.php",
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URL = "http://his.diva-portal.org/smash/record.jsf?pid=diva2%3A32473&dswid=-5602",
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size = "6 pages",
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abstract = "A common problem when using complicated models for
prediction and classification is that the complexity of
the model entails that it is hard, or impossible, to
interpret. For some scenarios this might not be a
limitation, since the priority is the accuracy of the
model. In other situations the limitations might be
severe, since additional aspects are important to
consider; e.g. comprehensibility or scalability of the
model. In this study we show how the gap between
accuracy and other aspects can be bridged by using a
rule extraction method (termed G-REX) based on genetic
programming. The extraction method is evaluated against
the five criteria accuracy, comprehensibility,
fidelity, scalability and generality. It is also shown
how G-REX can create novel representation languages;
here regression trees and fuzzy rules. The problem used
is a data-mining problem from the marketing domain
where the impact of advertising is predicted from
investment plans. Several experiments, covering both
regression and classification tasks, are evaluated.
Results show that G-REX in general is capable of
extracting both accurate and comprehensible
representations, thus allowing high performance also in
domains where comprehensibility is of essence.",
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notes = "Any time rule extraction",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Ulf Johansson
Rikard Konig
Lars Niklasson
Citations