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

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, : 465--473, 2000.
Authors: Siddhartha Bhattacharyya
URL: http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530
Tags: Algorithms, Design, Experimentation, Factors, Human Management, Measurement, Pareto-optimal Performance, Theory, algorithms, computation, data database evolutionary genetic marketing, mining, models, multiple objectives programming,
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.
| URL | BibTeX  
@inproceedings{347186,
title = {Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing},
address = {Boston, Massachusetts, United States},
author = {Siddhartha Bhattacharyya},
booktitle = {KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining},
pages = {465--473},
publisher = {ACM Press},
url = {http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530},
year = {2000},
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.},
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_address = {New York, NY, USA}, size = {9 pages}, isbn = {1-58113-233-6}, 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{"}.}, doi = {doi:10.1145/347090.347186},
keywords = {Algorithms, Design, Experimentation, Factors, Human Management, Measurement, Pareto-optimal Performance, Theory, algorithms, computation, data database evolutionary genetic marketing, mining, models, multiple objectives programming, }
}