An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first formulate this learning problem as a constrained optimization problem and then converse it to an unconstrained Multi-objective Optimization Problem (MOP). A parallel Multi-Objective Evolutionary Algorithm (MOEA) on consumer-level graphics hardware is used to handle 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.
|Title of host publication||2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010|
|ISBN (Electronic)||9781424469116, 9781424469109|
|Publication status||Published - Jul 2010|
|Event||2010 IEEE Congress on Evolutionary Computation (CEC) - Barcelona, Spain|
Duration: 18 Jul 2010 → 23 Jul 2010
|Conference||2010 IEEE Congress on Evolutionary Computation (CEC)|
|Period||18/07/10 → 23/07/10|
Bibliographical notePaper presented at the 2010 IEEE World Congress on Computational Intelligence, Jul 18-23, 2010, Barcelona, Spain.
WONG, M. L., & CUI, G. (2010). Data mining using parallel multi-objective evolutionary algorithms on graphics hardware. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 IEEE. https://doi.org/10.1109/CEC.2010.5586161