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Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation

  • Siddhartha Bhattacharyya
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)

Abstract

Predictive models find wide use in marketing for customer segmentation, targeting, etc. Models can be developed to different objectives, as defined through the dependent variable of interest. While standard modeling approaches embody single performance objectives, actual marketing decisions often need consideration of multiple performance criteria. Multiple objective problems typically characterize a range of solutions, none of which dominate the others with respect to the different objectives - these specify the Pareto-frontier of non-dominated solutions, each offering a different level of tradeoff. This chapter examines the use of evolutionary computation to obtain a set of such non-dominated models. An application using a real-life problem and data-set is presented, with results highlighting how such multi-objective models can yield advantages over traditional approaches.

Keywords

Genetic Algorithm Evolutionary Computation Multiobjective Optimization Pareto Frontier Population Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Berry, M.J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales and Customer Relationship Management. John Wiley & Sons, Chichester (2004)Google Scholar
  2. Becerra, R.L., Santana-Quintero, L.V., Coello, C.C.: Knowledge Incorporation in Multi-objective Evolutionary Algorithms. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds.) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol. 98, pp. 23–46. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. Bhattachryya, S.: Direct Marketing Performance Modeling using Genetic Algorithms. INFORMS Journal of Computing 11(13), 248–257 (1999)CrossRefGoogle Scholar
  4. Bhattacharyya, S.: Evolutionary algorithms in data mining: Multi-objective performance modeling for direct marketing. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, pp. 465–473 (2000)Google Scholar
  5. Casillas, J., Martínez-López, F.J.: Mining Uncertain Data with Multiobjective Genetic Fuzzy Systems to be Applied in Consumer Behaviour Modeling. Expert Systems with Applications 36(2), 1645–1659 (2009)CrossRefGoogle Scholar
  6. Coello, C.C.: An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Surveys 32(2), 109–143 (2000)CrossRefGoogle Scholar
  7. Coello, C.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer, New York (2006)Google Scholar
  8. De La Iglesia, B., Richards, G., Philpott, M.S., Rayward-Smith, V.J.: The Application and Effectiveness of a Multi-objective Metaheuristic Algorithm for Partial Classification. European Journal of Operational Research 169(3), 898–917 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  9. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)zbMATHGoogle Scholar
  10. Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  11. Dehuri, S., Ghosh, S., Ghosh, A.: Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds.) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol. 98, pp. 1–22. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. Dehuri, S., Jagadev, A.K., Ghosh, A., Mall, R.: Multi-objective Genetic Algorithm for Association Rule Mining using a Homogeneous Dedicated Cluster of Workstations. American Journal of Applied Sciences 88, 2086–2095 (2006)Google Scholar
  13. Dehuri, S., Mall, R.: Predictive and Comprehensible Rule Discovery using a Multi-objective Genetic Algorithm. Knowledge-Based Systems 19(6), 413–421 (2006)CrossRefGoogle Scholar
  14. Evett, M., Fernandez, T.: Numeric Mutation Improves the Discovery of Numeric Constants in Genetic Program. In: Koza, J.R., et al. (eds.) Proceedings of the Third Annual Genetic Programming Conference, Wisconsin, Madison, Morgan Kaufmann, San Francisco (1998)Google Scholar
  15. Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multi-Objective Optimization. Evolutionary Computation 3(1), 1–16 (1995)CrossRefGoogle Scholar
  16. Freitas, A.A.: A Critical Review of Multi-objective Optimization in Data Mining: a Position Paper. SIGKDD Explorations. Newsletter 6(2), 77–86 (2004)CrossRefMathSciNetGoogle Scholar
  17. Freitas, A.A., Pappa, G.L., Kaestner, C.A.A.: Attribute Selection with a Multi-objective Genetic Algorithm. In: Proceedings of the 16th Brazilian Symposium on Artificial Intelligence, pp. 280–290. Springer, Heidelberg (2002)Google Scholar
  18. Ghosh, A., Nath, B.: Multi-objective Rule mining using Genetic Algorithms. Information Sciences 163(1-3), 123–133 (2004)CrossRefMathSciNetGoogle Scholar
  19. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  20. Goldberg, D.E., Richardson, K.: Genetic algorithms with Sharing for Multi-modal Function Optimization. In: Proceedings of the 2nd International Conference on Genetic Algorithm, pp. 41–49 (1987)Google Scholar
  21. Hand, D.J.: Construction and Assessment of Classification Rules. John Wiley and Sons, Chichester (1997)zbMATHGoogle Scholar
  22. Handl, J., Knowles, J.: Multiobjective Clustering with Automatic Determination of the Number of Clusters, Technical Report No. TR-COMPSYSBIO-2004-02, UMIST, Department of Chemistry (August 2004)Google Scholar
  23. Kaya, M.: Multi-objective Genetic Algorithm based Approaches for Mining Optimized Fuzzy Association Rules. Soft Computing: A Fusion of Foundations, Methodologies and Applications 10(7), 578–586 (2006)zbMATHMathSciNetGoogle Scholar
  24. Kim, D.: Structural Risk Minimization on Decision Trees using an Evolutionary Multiobjective Algorithm. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 338–348. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  25. Kim, Y., Street, N.W.: An Intelligent System for Customer Targeting: a Data Mining Approach. Decision Support Systems 37(2), 215–228 (2004)Google Scholar
  26. Kim, Y., Street, W.N., Menczer, F.: Feature Selection in Unsupervised Learning via Evolutionary Search. In: Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2000), pp. 365–369 (2000)Google Scholar
  27. Knowles, J.D., Corne, D.W.: Approximating the Non-dominated Front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 49–172 (2000)CrossRefGoogle Scholar
  28. Kollat, J.B., Reed, P.M.: The value of online adaptive search: A performance comparison of NSGAII, ε-NSGAII and εMOEA. In: Coello, C.C., Aguirre, A.H., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 386–398. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  29. Kollat, J.B., Reed, P.M.: A framework for Visually Interactive Decision-making and Design using Evolutionary Multi-objective Optimization (VIDEO). Environmental Modelling & Software 22(12), 1691–1704 (2007)CrossRefGoogle Scholar
  30. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1993)Google Scholar
  31. Louis, S.J., Rawlins, G.J.E.: Pareto-Optimality, GA-Easiness and Deception. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 118–123 (1993)Google Scholar
  32. Massand, B., Piatetsky-Shapiro, G.: A Comparison of Different Approaches for Maximizing the Business Payoffs of Prediction Models. In: Simoudis, E., Han, J.W., Fayyad, U. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 195–201 (1996)Google Scholar
  33. Menczer, F., Degeratu, M., Street, N.W.: Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms. Evolutionary Computation 8(2), 223–247 (2000)CrossRefGoogle Scholar
  34. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 2nd edn. Springer, Heidelberg (1994)zbMATHGoogle Scholar
  35. Murty, M.N., Babaria, R., Bhattacharyya, C.: Clustering Based on Genetic Algorithms. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds.) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol. 98, pp. 137–159. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  36. Pappa, G.L., Freitas, A.A.: Evolving Rule Induction algorithms with Multi-objective Grammar-based Genetic Programming. Knowledge and Information Systems 19(3), 283–309 (2009)CrossRefGoogle Scholar
  37. Richardson, J.T., Palmer, M.R., Liepins, G., Hilliard, M.: Some Guidelines for Genetic Algorithms with Penalty Functions. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on genetic Algorithms, pp. 191–197 (1989)Google Scholar
  38. Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Mahwah (1985)Google Scholar
  39. Shaw, K.J., Nortcliffe, A.L., Thompson, M., Love, J., Fonseca, C.M., Fleming, P.J.: Assessing the Performance of Multiobjective Genetic Algorithms for Optimization of a Batch Process Scheduling Problem. In: Angeline, P. (ed.) Congress on Evolutionary Computation, pp. 37–45. IEEE Press, Piscataway (1999)Google Scholar
  40. Sikora, R., Piramuthu, S.: Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance. Information Technology and Management 6(4), 315–331 (2005)CrossRefGoogle Scholar
  41. Thilagam, P.S., Ananthanarayana, V.S.: Extraction and Optimization of Fuzzy Association Rules using Multi-objective Genetic Algorithm. Pattern Analysis and Applications 11(2), 159–168 (2008)CrossRefMathSciNetGoogle Scholar
  42. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Test Suites. In: Carroll, J., Haddad, H., Oppenheim, D., Bryant, B., Lamont, G.B. (eds.) Proceedings of the 1999 ACM Symposium on Applied Computing, New York, pp. 351–357 (1999)Google Scholar
  43. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-Art. Evolutionary Computation 8(2), 125–147 (2000)CrossRefGoogle Scholar
  44. Zhang, Y., Bhattacharyya, S.: Genetic Programming in Classifying Large-scale Data: an Ensemble Method. Information Sciences 163(1-3), 85–101 (2004)CrossRefGoogle Scholar
  45. Zeleny, M.: Multiple Criteria Decision Making. McGraw-Hill, New York (1982)Google Scholar
  46. Zitzler, E., Thiele, L.: Multi-objective Evolutionary Algorithms: a Comparative Case study and Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Siddhartha Bhattacharyya
    • 1
  1. 1.Information and Decision Sciences, College of Business AdministrationUniversity of IllinoisChicago

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