Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression

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@Article{Kromer:2013:CMA,

• author = "P. Kromer and S. Owais and J. Platos and V. Snasel",
• title = "Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression",
• journal = "Computer and Mathematics with Applications",
• year = "2013",
• volume = "66",
• number = "2",
• month = aug,
• pages = "190--200",
• keywords = "genetic algorithms, genetic programming, Fuzzy rules, Fuzzy information retrieval, Data mining, Application",
• ISSN = "0898-1221",
• DOI = "doi:10.1016/j.camwa.2013.02.017",
• URL = "http://www.sciencedirect.com/science/article/pii/S0898122113001284",
• size = "11 pages",
• 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