Evolutionary algorithms in data mining
Siddhartha Bhattacharyya · 2000
Predictive models in direct marketing seek to identify individuals most likely to respond to promotional solicitations or other intervention programs.While standard modeling approaches embody single objectives, real-world decision problems often seek multiple performance measures.Decision-makers here desire solutions that simultaneously optimize on multiple objectives, or obtain an acceptable tradeoff amongst objectives.Multi-criteria problems often characterize 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.