Knowledge and Information Systems

, Volume 7, Issue 2, pp 246–266

Multiknowledge for decision making


    • School of Computing and Intelligent SystemsUniversity of Ulster at Magee
  • David Bell
    • School of Computer ScienceQueens University
  • Martin McGinnity
    • School of Computing and Intelligent SystemsUniversity of Ulster at Magee

DOI: 10.1007/s10115-004-0150-0

Cite this article as:
Wu, Q., Bell, D. & McGinnity, M. Knowl Inf Syst (2005) 7: 246. doi:10.1007/s10115-004-0150-0


The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.


ClassificationDecision makingDecision systemKnowledge representationMultiknowledgeMultiple reductsRough setUncertain rule
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© Springer-Verlag 2004