Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression
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- @Article{Kromer:2013:CMA,
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author = "P. Kromer and S. Owais and J. Platos and V. Snasel",
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title = "Towards new directions of data mining by evolutionary
fuzzy rules and symbolic regression",
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journal = "Computer and Mathematics with Applications",
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year = "2013",
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volume = "66",
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number = "2",
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month = aug,
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pages = "190--200",
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keywords = "genetic algorithms, genetic programming, Fuzzy rules,
Fuzzy information retrieval, Data mining, Application",
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ISSN = "0898-1221",
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DOI = "doi:10.1016/j.camwa.2013.02.017",
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URL = "http://www.sciencedirect.com/science/article/pii/S0898122113001284",
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size = "11 pages",
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abstract = "There are various techniques for data mining and data
analysis. Among them, hybrid approaches combining two
or more fundamental methods gain importance as the
complexity and dimension of real world problems and
data sets grows. Fuzzy sets and fuzzy logic can be used
for efficient data classification by the means of fuzzy
rules and classifiers. This study presents an
application of genetic programming to the evolution of
fuzzy rules based on the concept of extended Boolean
queries. Fuzzy rules are used as symbolic classifiers
learnt from data and used to label data records and to
predict the value of an output variable. An example of
the application of such a hybrid evolutionary-fuzzy
data mining approach to a real world problem is
presented.",
- }
Genetic Programming entries for
Pavel Kromer
Suhail S J Owais
Jan Platos
Vaclav Snasel
Citations