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A machine learning method for multi-expert decision support

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  • Clyde Holsapple
  • Anita Lee
  • Jim Otto

Abstract

When a decision maker has access to multiple expert systems, each embodying a different expert perspective on analyzing and reasoning about the same kind of decision problem, an important consideration is which to use at what times. We address this issue with a method based on competition among the distinct expert systems (and their respective rules). We begin by reviewing prior research concerned with the coordination of multiple sources of expertise in support of decision making, pointing out potential weaknesses of the proposed methods. Next, we introduce a new coordination method based on the competitive paradigm that has been applied in machine learning. This method involves adjustments to the strengths of expert systems and to their constituent rules based on their performances. A nine-step process for adjusting strengths is described. Advantages and limitations of this new method for expert system coordination are discussed. We outline an approach to testing the coordination method and report on preliminary testing of the performance of a system employing our method versus the performance of individual experts. Copyright Kluwer Academic Publishers 1997

Suggested Citation

  • Clyde Holsapple & Anita Lee & Jim Otto, 1997. "A machine learning method for multi-expert decision support," Annals of Operations Research, Springer, vol. 75(0), pages 171-188, January.
    • Handle: RePEc:spr:annopr:v:75:y:1997:i:0:p:171-188:10.1023/a:1018955328719
    DOI: 10.1023/A:1018955328719
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    Cited by:

    1. Yuan Li & Xiuwu Liao & Wenhong Zhao, 2009. "A rough set approach to knowledge discovery in analyzing competitive advantages of firms," Annals of Operations Research, Springer, vol. 168(1), pages 205-223, April.
    2. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    3. Eduardo R. Hruschka & Estevam R. Hruschka & Thiago F. Covões & Nelson F. F. Ebecken, 2006. "Bayesian Feature Selection for Clustering Problems," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 315-327.
    4. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    5. Weiwei Chen & Jie Song & Leyuan Shi & Liang Pi & Peter Sun, 2013. "Data mining-based dispatching system for solving the local pickup and delivery problem," Annals of Operations Research, Springer, vol. 203(1), pages 351-370, March.

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