Evolving Accurate and Comprehensible Classification Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Sonstrod:2011:EAaCCR,
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title = "Evolving Accurate and Comprehensible Classification
Rules",
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author = "Cecilia Sonstrod and Ulf Johansson and Rikard Konig",
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pages = "1435--1442",
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booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
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year = "2011",
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editor = "Alice E. Smith",
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month = "5-8 " # jun,
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address = "New Orleans, USA",
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming,
Classification, clustering, data analysis and data
mining, Learning classifier systems",
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DOI = "doi:10.1109/CEC.2011.5949784",
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abstract = "In this paper, Genetic Programming is used to evolve
ordered rule sets (also called decision lists) for a
number of benchmark classification problems, with
evaluation of both predictive performance and
comprehensibility. The main purpose is to compare this
approach to the standard decision list algorithm JRip
and also to evaluate the use of different length
penalties and fitness functions for evolving this type
of model. The results, using 25 data sets from the UCI
repository, show that genetic decision lists with
accuracy-based fitness functions outperform JRip
regarding accuracy. Indeed, the best setup was
significantly better than JRip. JRip, however, held a
slight advantage over these models when evaluating AUC.
Furthermore, all genetic decision list setups produced
models that were more compact than JRip models, and
thus more readily comprehensible. The effect of using
different fitness functions was very clear; in essence,
models performed best on the evaluation criterion that
was used in the fitness function, with a worsening of
the performance for other criteria. Brier score fitness
provided a middle ground, with acceptable performance
on both accuracy and AUC. The main conclusion is that
genetic programming solves the task of evolving
decision lists very well, but that different length
penalties and fitness functions have immediate effects
on the results. Thus, these parameters can be used to
control the trade-off between different aspects of
predictive performance and comprehensibility.",
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notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
- }
Genetic Programming entries for
Cecilia Sonstrod
Ulf Johansson
Rikard Konig
Citations