[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation

  • Chapter
Marketing Intelligent Systems Using Soft Computing

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

  • 1814 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 103.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Berry, M.J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales and Customer Relationship Management. John Wiley & Sons, Chichester (2004)

    Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • Bhattachryya, S.: Direct Marketing Performance Modeling using Genetic Algorithms. INFORMS Journal of Computing 11(13), 248–257 (1999)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  Google Scholar 

  • Coello, C.C.: An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Surveys 32(2), 109–143 (2000)

    Article  Google Scholar 

  • 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 

  • 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)

    Article  MATH  MathSciNet  Google Scholar 

  • Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)

    MATH  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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 

  • Dehuri, S., Mall, R.: Predictive and Comprehensible Rule Discovery using a Multi-objective Genetic Algorithm. Knowledge-Based Systems 19(6), 413–421 (2006)

    Article  Google Scholar 

  • 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 

  • Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multi-Objective Optimization. Evolutionary Computation 3(1), 1–16 (1995)

    Article  Google Scholar 

  • Freitas, A.A.: A Critical Review of Multi-objective Optimization in Data Mining: a Position Paper. SIGKDD Explorations. Newsletter 6(2), 77–86 (2004)

    Article  MathSciNet  Google Scholar 

  • 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 

  • Ghosh, A., Nath, B.: Multi-objective Rule mining using Genetic Algorithms. Information Sciences 163(1-3), 123–133 (2004)

    Article  MathSciNet  Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  • 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 

  • Hand, D.J.: Construction and Assessment of Classification Rules. John Wiley and Sons, Chichester (1997)

    MATH  Google Scholar 

  • 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 

  • 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)

    MATH  MathSciNet  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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 

  • 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 

  • Knowles, J.D., Corne, D.W.: Approximating the Non-dominated Front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 49–172 (2000)

    Article  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1993)

    Google Scholar 

  • 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 

  • 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 

  • Menczer, F., Degeratu, M., Street, N.W.: Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms. Evolutionary Computation 8(2), 223–247 (2000)

    Article  Google Scholar 

  • Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 2nd edn. Springer, Heidelberg (1994)

    MATH  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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 

  • Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-Art. Evolutionary Computation 8(2), 125–147 (2000)

    Article  Google Scholar 

  • Zhang, Y., Bhattacharyya, S.: Genetic Programming in Classifying Large-scale Data: an Ensemble Method. Information Sciences 163(1-3), 85–101 (2004)

    Article  Google Scholar 

  • Zeleny, M.: Multiple Criteria Decision Making. McGraw-Hill, New York (1982)

    Google Scholar 

  • 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bhattacharyya, S. (2010). Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation. In: Casillas, J., Martínez-López, F.J. (eds) Marketing Intelligent Systems Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15606-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15606-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15605-2

  • Online ISBN: 978-3-642-15606-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics