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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/7050

Title: A comparison of some AI and statistical classification methods for a marketing case
Authors: Montgomery, D.
SWINNEN, Gilbert
Issue Date: 1997
Publisher: Elsevier Science B.V.
Citation: European journal of operational research, 103(2). p. 312-325
Abstract: Recent progress in data processing technology has made the accumulation and systematic organization of large volumes of data a routine activity. As a result of these developments, there is an increasing need for data-based or data-driven methods of model development. This paper describes data-driven classification methods and shows that the automatic development and refinement of decision support models is now possible when the machine is given a large (or sometimes even a small) amount of observations that express instances of a certain task domain. The classifier obtained may be used to build a decision support system, to refine or update an existing system and to understand or improve a decision-making process. The described AI classification methods are compared with statistical classification methods for a marketing application. They can act as a basis for data-driven decision support systems that have two basic components: an automated knowledge module and an advice module or, in different terms, an automated knowledge acquisition/retrieval module and a knowledge processing module. When these modules are integrated or linked, a decision support system can be created which enables an organization to make better-quality decisions, with reduced variance, probably using fewer people.
URI: http://hdl.handle.net/1942/7050
DOI: 10.1016/S0377-2217(97)00122-7
Type: Journal Contribution
Appears in Collections: Research publications

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