Learning non-overlapping rules A method based on Functional Dependency Network and MDL Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Shum:2006:CEC,
  author =       "Wing-Ho Shum and Kwong-Sak Leung and Man-Leung Wong",
  title =        "Learning non-overlapping rules A method based on
                 Functional Dependency Network and MDL Genetic
                 Programming",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "2717--2724",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, poster",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688380",
  size =         "8 pages",
  abstract =     "Classification rule is a useful model in data mining.
                 Given variable values, rules classify data items into
                 different classes. Different rule learning algorithms
                 are proposed, like Genetic Algorithm (GA) and Genetic
                 Programming (GP). Rules can also be extracted from
                 Bayesian Network (BN) and decision trees. However, all
                 of them have disadvantages and may fail to get the best
                 results. Both of GA and GP cannot handle cooperation
                 among rules and thus, the learnt rules are likely to
                 have many overlappings, i.e. more than one rules
                 classify the same data items and different rules have
                 different predictions. The conflicts among the rules
                 reduce their understandability and increase their usage
                 difficulty for expert systems. In contrast, rules
                 extracted from BN and decision trees have no
                 overlapping in nature. But BN can handle discrete
                 values only and cannot represent higher-order
                 relationships among variables. Moreover, the search
                 space for decision tree learning is huge and thus, it
                 is difficult to reach the global optimum. In this
                 paper, we propose to use Functional Dependency Network
                 (FDN) and MDL Genetic Programming (MDLGP) to learn a
                 set of non-overlapping classification rules [17]. The
                 FDN is an extension of BN; it can handle all kind of
                 values; it can represent higher-order relationships
                 among variables; and its learning search space is
                 smaller than decision trees'. The experimental results
                 demonstrate that the proposed method can successfully
                 discover the target rules, which have no overlapping
                 and have the highest classification accuracies.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Wing-Ho Shum Kwong-Sak Leung Man Leung Wong

Citations