Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units
An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profits to a company under resource constraints. In this chapter, we first formulate this learning problem as a co
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Abstract An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profits to a company under resource constraints. In this chapter, we first formulate this learning problem as a constrained optimization problem and then convert it to an unconstrained multi-objective optimization problem (MOP), which can be handled by some multi-objective evolutionary algorithms (MOEAs). However, MOEAs may execute for a long time for the MOP, because several evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. Thus we propose a parallel MOEA on consumer-level graphics processing units (GPU) to tackle the MOP. We perform experiments on a real-life direct marketing problem to compare the proposed method with the parallel hybrid genetic algorithm, the DMAX approach, and a sequential MOEA. It is observed that the proposed method is much more effective and efficient than the other approaches.
1 Introduction How to improve marketing productivity or the return on marketing investment under resource constraints is one of the most challenging issues facing marketing professionals and researchers. The issue seems to be more pressing in hard economic times and given the increasing emphasis on customer relationship management—containing cost and channeling precious marketing resources
M.L. Wong () Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong e-mail: [email protected] G. Cui Department of Marketing and International Business, Lingnan University, Tuen Mun, Hong Kong e-mail: [email protected] S. Tsutsui and P. Collet (eds.), Massively Parallel Evolutionary Computation on GPGPUs, Natural Computing Series, DOI 10.1007/978-3-642-37959-8 14, © Springer-Verlag Berlin Heidelberg 2013
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to the high-value customers who contribute greater profit to a company. Such situations include (1) upgrading customers—how to provide sizable incentives to the customers who are the most likely to upgrade and contribute greater profit over the long run? (2) modeling customer churn or retention—how to identify and prevent the most valuable customers from switching to a competitor? and (3) predicting loan default—how to predict the small minority who default on their large loans or credit card bills? This problem is particularly acute in direct marketing operations that typically have a fixed budget to target, from the vast list of customers in a company’s database, those customers who are the most likely to respond to a marketing campaign and purchase greater amounts. Most marketing activities, as espoused by marketing scholars and practitioners, are targeted marketing in nature—to reach customers who are the most responsive to marketing activities. Until recently, statistical methods such as regression and discriminant analysis have dominated the modeling of consumer responses to marketing activities. These methods, however, suffer from two shortcomings.
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