Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing

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@InProceedings{347186,
  author =       "Siddhartha Bhattacharyya",
  title =        "Evolutionary algorithms in data mining:
                 multi-objective performance modeling for direct
                 marketing",
  booktitle =    "KDD '00: Proceedings of the sixth ACM SIGKDD
                 international conference on Knowledge discovery and
                 data mining",
  year =         "2000",
  pages =        "465--473",
  address =      "Boston, Massachusetts, United States",
  publisher_address = "New York, NY, USA",
  organisation = "SIGKDD: ACM Special Interest Group on Knowledge
                 Discovery in Data AAAI : Am Assoc for Artifical
                 Intelligence SIGART: ACM Special Interest Group on
                 Artificial Intelligence SIGMOD: ACM Special Interest
                 Group on Management of Data",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming, Algorithms,
                 Design, Experimentation, Human Factors, Management,
                 Measurement, Performance, Theory, Pareto-optimal
                 models, data mining, database marketing, evolutionary
                 computation, multiple objectives",
  ISBN =         "1-58113-233-6",
  URL =          "http://tigger.uic.edu/~sidb/papers/MultiObj_KDD2000.pdf",
  URL =          "http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530",
  DOI =          "doi:10.1145/347090.347186",
  size =         "9 pages",
  abstract =     "Predictive models in direct marketing seek to identify
                 individuals most likely to respond to promotional
                 solicitations or other intervention programs. While
                 standard modelling approaches embody single objectives,
                 real-world decision problems often seek multiple
                 performance measures. Decision-makers here desire
                 solutions that simultaneously optimise on multiple
                 objectives, or obtain an acceptable tradeoff amongst
                 objectives. Multi-criteria problems often characterise
                 a range of solutions, none of which dominate the others
                 with respect to the multiple objectives - these specify
                 the Pareto-frontier of nondominated solutions, each
                 offering a different level of tradeoff. This paper
                 proposes the use of evolutionary computation based
                 procedures for obtaining a set of nondominated models
                 with respect to multiple stated objectives. The
                 targeting depth-of-file presents a crucial real-world
                 criterion in direct marketing, and models here are
                 tailored for specified file-depths. Decision-makers are
                 thus able to obtain a set of models along the
                 Pareto-frontier, for a specific file-depth. The choice
                 of a model to implement can be thus based on observed
                 tradeoffs in the different objectives, based on
                 possibly subjective and problem specific judgements.
                 Given distinct models tailored for different
                 file-depths, the implementation decision can also
                 consider performance tradeoffs at the different
                 depths-offile. Empirical results from a real-world
                 problem illustrate the benefits of the proposed
                 approach. Both linear and nonlinear models obtained by
                 genetic search are examined.",
  notes =        "p470 {"}For the non-linear GP, results were found to
                 be similar to those observed for the linear GA.
                 {"}Elitism always provides improved performance{"}.",
}

Genetic Programming entries for Siddhartha Bhattacharyya

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