Conference proceeding
An evolutionary multi-objective local selection algorithm for customer targeting
PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, Vol.2, pp.759-766
IEEE Congress on Evolutionary Computation
01/01/2001
DOI: 10.1109/CEC.2001.934266
Abstract
In an increasingly competitive marketplace, one of the most interesting and challenging problems is how to identify and profile customers who are most likely to be interested in new products or services. At the same time, minimizing the number of variables used in the prediction task is important with large databases. In this paper we consider a novel application of evolutionary multiobjective algorithms for customer targeting. Evolutionary algorithms are considered effective in solving multiobjective problems because of their inherent parallelism. We use ELSA, an evolutionary local selection algorithm that maintains a diverse population of solutions approximating the Pareto front in a multi-dimensional objective space. We use artificial neural networks (ANNs) for customer prediction and ELSA to search for promising subsets of features. Our results on a real data set show that our approach is easier to interpret and more accurate than the traditional method used in marketing.
Details
- Title: Subtitle
- An evolutionary multi-objective local selection algorithm for customer targeting
- Creators
- Y Kim - University of IowaW N Street - University of IowaF Menczer - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, Vol.2, pp.759-766
- Publisher
- IEEE
- Series
- IEEE Congress on Evolutionary Computation
- DOI
- 10.1109/CEC.2001.934266
- Number of pages
- 8
- Language
- English
- Date published
- 01/01/2001
- Academic Unit
- Nursing; Business Analytics; Computer Science; Bus Admin College
- Record Identifier
- 9984380437402771
Metrics
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