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

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@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

Citations