Applied Intelligence

, Volume 49, Issue 2, pp 804–818 | Cite as

Knowledge acquisition and decision making based on Bayes risk minimization method

  • Mingliang SuoEmail author
  • Zhiping Zhang
  • Ying Chen
  • Ruoming An
  • Shunli LiEmail author


There are two central parts in multiple attribute decision making (MADM), which are weight assignment and attribute selection. However, attribute selection is usually ignored in the existing researches, which will result in the difficulty of knowledge acquisition and the error of decision making. In addition, with respect to the data set with labels, the existing methods of weight assignment usually neglect or do not take full advantage of the supervisory function of labels, which may also lead to some decision making mistakes. To make up for these deficiencies, this paper proposes a method for knowledge acquisition and decision making based on Bayes risk minimization. In this method, a novel Bayes risk model based on neighborhood and Gaussian kernel is raised, and a heuristic forward greedy algorithm is designed for attribute selection. Finally, a number of experiments, including the comparison experiments on University of California Irvine (UCI) data and the effectiveness evaluation of fighter, are carried out to illustrate the superiority and applicability of the proposed method.


Multiple attribute decision making Bayes risk minimization Weight assignment Attribute selection Effectiveness evaluation 



Thanks to all anonymous reviewers for their guidance and comments to this paper. This study is supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT.KLOF.2017.074).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Reliability and Systems EngineeringBeihang UniversityBeijingChina
  2. 2.Science, Technology on ReliabilityEnvironmental Engineering LaboratoryBeijingChina
  3. 3.School of AstronauticsHarbin Institute of TechnologyHarbinChina

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