Genetically Constructed Kernels for Support Vector Machines

Stefan Lessmann, Robert Stahlbock and Sven Crone
Additional contact information
Stefan Lessmann: University of Hamburg
Robert Stahlbock: University of Hamburg
Sven Crone: Lancaster University Management School

A chapter in Operations Research Proceedings 2005, 2006, pp 257-262 from Springer

Abstract: Abstract Data mining for customer relationship management involves the task of binary classification, e.g. to distinguish between customers who are likely to respond to direct mail and those who are not. The support vector machine (SVM) is a powerful learning technique for this kind of problem. To obtain good classification results the selection of an appropriate kernel function is crucial for SVM. Recently, the evolutionary construction of kernels by means of meta-heuristics has been proposed to automate model selection. In this paper we consider genetic algorithms (GA) to generate SVM kernels in a data driven manner and investigate the potential of such hybrid algorithms with regard to classification accuracy, generalisation ability of the resulting classifier and computational efficiency. We contribute to the literature by: (1) extending current approaches for evolutionary constructed kernels; (2) investigating their adequacy in a real world business scenario; (3) considering runtime issues together with measures of classification effectiveness in a mutual framework.

Keywords: Support Vector Machine; Customer Relationship Management; Direct Marketing; Support Vector Machine Parameter; Bayesian Neural Network (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-540-32539-0_41

Ordering information: This item can be ordered from
http://www.springer.com/9783540325390

DOI: 10.1007/3-540-32539-5_41

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().