Fuzzy Association Rule Mining and Classifier with Chi-squared Correlation Measure using Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Taboada:2009:ICCAS-SICE,
  author =       "Karla S Taboada and Shingo Mabu and Eloy Gonzales and 
                 Kaoru Shimada and Kotaro Hirasawa",
  title =        "Fuzzy Association Rule Mining and Classifier with
                 Chi-squared Correlation Measure using Genetic Network
                 Programming",
  booktitle =    "ICRAS \& SICE International Joint Conference,
                 ICCAS-SICE, 2009",
  year =         "2009",
  month =        "18-21 " # aug,
  address =      "Fukuoka",
  pages =        "3863--3869",
  publisher =    "IEEE",
  isbn13 =       "978-4-9077-6433-3",
  keywords =     "genetic algorithms, genetic programming, chi-squared
                 correlation measure, correlation analysis, directed
                 graph structure, discovered rules evaluation,
                 evolutionary optimization algorithm, fuzzy association
                 rule mining, genetic network programming, statistical
                 significance, support confidence framework, correlation
                 methods, data mining, directed graphs, fuzzy set
                 theory, pattern classification",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5332929",
  size =         "7 pages",
  abstract =     "One of the most important issues in any association
                 rule mining is the interpretation and evaluation of
                 discovered rules. Thus, most algorithms employ the
                 support-confidence framework for evaluating association
                 and classification rules. Unfortunately, recent studies
                 show that the support and confidence measures are
                 insufficient for filtering out uninteresting
                 association rules, for instance, even strong
                 association rules can be uninteresting and misleading.
                 To deal with this limitation, the support-confidence
                 framework can be supplemented with additional
                 interestingness measures based on statistical
                 significance and correlation analysis. In this paper, a
                 novel fuzzy association rule-based classification
                 approach is proposed, where chi2 is applied as a
                 correlation measure. The algorithm is based on Genetic
                 Network Programming (GNP) and discover comprehensible
                 fuzzy association rules potentially useful for
                 classification. GNP is an evolutionary optimization
                 algorithm that uses directed graph structures as genes
                 instead of strings and trees of Genetic Algorithms (GA)
                 and Genetic Programming (GP), respectively. This
                 feature contributes to creating quite compact programs
                 and implicitly memorizing past action sequences. The
                 proposed model consists of two major phases: 1
                 generating fuzzy class association rules by using GNP,
                 2 building a classifier based on the extracted fuzzy
                 rules. In the first phase, chi2 is used for computing
                 the correlation of the rules to be integrated into the
                 classifier. In the second phase, the chi2 value is used
                 as a weight of the rule when calculating the matching
                 degree of the rule with new data. The performance of
                 the proposed algorithm has been compared with other
                 relevant algorithms and the experimental results have
                 shown the advantages and effectiveness of the proposed
                 model.",
  notes =        "http://www.sice.or.jp/ICCAS-SICE2009/session_paperID_c.pdf
                 Also known as \cite{5332929}",
}

Genetic Programming entries for Karla Taboada Shingo Mabu Eloy Gonzales Kaoru Shimada Kotaro Hirasawa

Citations