Created by W.Langdon from gp-bibliography.bib Revision:1.4067
@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