Evolutionary Data Mining Approaches for Rule-based and Tree-based Classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Weise:2010:ICCI,

• author = "Thomas Weise and Raymond Chiong",
• title = "Evolutionary Data Mining Approaches for Rule-based and Tree-based Classifiers",
• booktitle = "9th IEEE International Conference on Cognitive Informatics (ICCI 2010)",
• year = "2010",
• editor = "Fuchun Sun and Yingxu Wang and Jianhua Lu and Bo Zhang and Witold Kinsner and Lotfi A. Zadeh",
• pages = "696--703",
• address = "Tsinghua University, Beijing, China",
• month = "7-9 " # jul,
• publisher = "IEEE",
• note = "Special Session on Evolutionary Computing",
• email = "tweise@gmx.de",
• keywords = "genetic algorithms, genetic programming, data mining, decision trees, rule-based classifiers, C4.5 approach, decision trees, evolutionary algorithms, evolutionary data mining approach, random-forest approach, rule set encoding, rule-based classifier, supervised data mining approach, tree-based classifiers, data mining, knowledge based systems, pattern classification",
• isbn13 = "978-1-4244-8040-1",
• URL = "http://www.it-weise.de/documents/files/WC2010EDMAFRBATBC.pdf",
• DOI = "doi:10.1109/COGINF.2010.5599821",
• abstract = "Data mining is an important process, with applications found in many business, science and industrial problems. While a wide variety of algorithms have already been proposed in the literature for classification tasks in large data sets, and the majority of them have been proven to be very effective, not all of them are flexible and easily extensible. In this paper, we introduce two new approaches for synthesising classifiers with Evolutionary Algorithms (EAs) in supervised data mining scenarios. The first method is based on encoding rule sets with bit string genomes and the second one uses Genetic Programming to create decision trees with arbitrary expressions attached to the nodes. Comparisons with some sophisticated standard approaches, such as C4.5 and Random-Forest, show that the performance of the evolved classifiers can be very competitive. We further demonstrate that both proposed approaches work well across different configurations of the EAs.",
• notes = "http://www.icci2010.edu.cn/ Also known as \cite{5599821}",

}

Genetic Programming entries for Thomas Weise Raymond Chiong

Citations