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|Title: ||A comparison of some AI and statistical classification methods for a marketing case|
|Authors: ||Montgomery, D.|
|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.|
|Type: ||Journal Contribution|
|Appears in Collections: ||Research publications|
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