MEPAR-miner: Multi-expression programming for classification rule mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Adil Baykasoglu and Lale Ozbakir",
  title =        "MEPAR-miner: Multi-expression programming for
                 classification rule mining",
  journal =      "European Journal of Operational Research",
  volume =       "183",
  number =       "2",
  pages =        "767--784",
  year =         "2007",
  ISSN =         "0377-2217",
  DOI =          "DOI:10.1016/j.ejor.2006.10.015",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Classification rules, Multi-expression programming,
                 Evolutionary programming",
  abstract =     "Classification and rule induction are two important
                 tasks to extract knowledge from data. In rule
                 induction, the representation of knowledge is defined
                 as IF-THEN rules which are easily understandable and
                 applicable by problem-domain experts. In this paper, a
                 new chromosome representation and solution technique
                 based on Multi-Expression Programming (MEP) which is
                 named as MEPAR-miner (Multi-Expression Programming for
                 Association Rule Mining) for rule induction is
                 proposed. Multi-Expression Programming (MEP) is a
                 relatively new technique in evolutionary programming
                 that is first introduced in 2002 by Oltean and
                 Dumitrescu. MEP uses linear chromosome structure. In
                 MEP, multiple logical expressions which have different
                 sizes are used to represent different logical rules.
                 MEP expressions can be encoded and implemented in a
                 flexible and efficient manner. MEP is generally applied
                 to prediction problems; in this paper a new algorithm
                 is presented which enables MEP to discover
                 classification rules. The performance of the developed
                 algorithm is tested on nine publicly available binary
                 and n-ary classification data sets. Extensive
                 experiments are performed to demonstrate that
                 MEPAR-miner can discover effective classification rules
                 that are as good as (or better than) the ones obtained
                 by the traditional rule induction methods. It is also
                 shown that effective gene encoding structure directly
                 improves the predictive accuracy of logical IF-THEN

Genetic Programming entries for Adil Baykasoglu Lale Ozbakir