Skip to main content
SpringerLink
Log in

A New Hybrid Algorithm for Feature Selection and Its Application to Customer Recognition

  • Conference paper
Combinatorial Optimization and Applications (COCOA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4616))

  • 1469 Accesses

Abstract

This paper proposes a novel hybrid algorithm for feature selection. This algorithm combines a global optimization algorithm called the simulated annealing algorithm based nested partitions (NP/SA). The resulting hybrid algorithm NP/SA retains the global perspective of the nested partitions algorithm and the local search capabilities of the simulated annealing method. We also present a detailed application of the new algorithm to a customer feature selection problem in customer recognition of a life insurance company and it is found to have great computation efficiency and convergence speed.

This research work is supported by the Natural Science Fund of China ( # 70501022) and China Postdoctoral Science Foundation ( # 20060400169).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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.

References

  1. Olafsson, S., Yang, J.: Intelligent Partitioning for Feature Selection. INFORMS Journal on Computing 17(3), 339–355 (2005)

    Article  Google Scholar 

  2. Basu, A.: Perspectives on operations research in data and knowledge management. Eur. J. Oper. Res. 111, 1–14 (1998)

    Article  Google Scholar 

  3. Bradley, P.S., Fayyad, U.M., Mangasarian, O.L.: Mathematical programming for data mining: Formulations and challenges. INFORMS J. Comput. 11, 217–238 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  4. Oh, I.-S., Lee, J.-S., Moon, B.-R.: Hybrid genetic algorithms for feature selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)

    Article  Google Scholar 

  5. Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Motada, H., Liu, H. (eds.) Feature Selection, Construction, and Subset Selection: A Data Mining Perspective, pp. 117–136. Kluwer, Dordrecht (1998)

    Google Scholar 

  6. Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proc. 17th Internat. Conf. Mach. Learn, Stanford University, pp. 359–366. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  7. Kim, Y.S., Street, W.N., Menczer, F.: Feature selection in unsupervised learning via evolutionary search. In: Proc. 6th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, Boston, MA, pp. 365–369. ACM, New York (2000)

    Chapter  Google Scholar 

  8. Modrzejewski, M.: Feature selection using rough sets theory. In: Brazdil, P.B. (ed.) Proc. Eur. Conf. Mach. Learn., Vienna, Austria, pp. 213–226. Springer, Heidelberg (1993)

    Google Scholar 

  9. Skalak, D.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proc. 11th Internat. Conf. Mach. Learn., New Brunswick, NJ, pp. 293–301. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  10. Shi, L., Olafsson, S.: Nested partitions method for global optimization. Oper. Res. 48, 390–407 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  11. Shi, L., Olafsson, S., Sun, N.: New parallel randomized algorithms for the traveling salesman problem. Computers & Operations Research 26, 371–394 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  12. Shi, L., Men, S.: Optimal buffer allocation in production lines. IIE Transactions 35, 1–10 (2003)

    Article  Google Scholar 

  13. Shi, L., Olafsson, S., Chen, Q.: A new hybrid optimization algorithm. Computers & Industrial Engineering 36, 409–426 (1999)

    Article  Google Scholar 

  14. Shi, L., Olafsson, S., Chen, Q.: An optimization framework for product design. Management Science 47(2), 1681–1692 (2001)

    Article  Google Scholar 

  15. Ahmed, M.A., Alkhamis, T.M.: Simulation-based optimization using Simulated Annealing with ranking and selection. Computers & Operations Research 29, 387–402 (2002)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of System Engineering, Shanghai Jiao Tong University, 200052 Shanghai, China

    Luo Yan

  2. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Yu Changrui

Authors
  1. Luo Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Changrui
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Andreas Dress Yinfeng Xu Binhai Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yan, L., Changrui, Y. (2007). A New Hybrid Algorithm for Feature Selection and Its Application to Customer Recognition. In: Dress, A., Xu, Y., Zhu, B. (eds) Combinatorial Optimization and Applications. COCOA 2007. Lecture Notes in Computer Science, vol 4616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73556-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73556-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73555-7

  • Online ISBN: 978-3-540-73556-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation