An Inductive Inference Model to Elicit Noncompensatory Judgment Strategies

  • Jing Yin
  • Ling Rothrock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6761)


The proposed research developed a noncompensatory policy capturing technique to infer judgment rules (represented in disjunctive normal form) from available human data. The rule induction algorithm employs multiobjective Genetic Algorithm (GA) as its central search mechanism to enhance the induction and classification process. The quality of the induced rule set is measured by two criteria, fidelity (the degree to which the rule set reflects the judgment data they have been extracted from) and compactness (the simplicity of the rule set). An experimental study is conducted to demonstrate the effectiveness of the algorithm on a number of benchmark datasets.


Disjunctive Normal Form Judgment Data Rule Induction Algorithm Judgment Behavior Noncompensatory Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Yin
    • 1
  • Ling Rothrock
    • 2
  1. 1.MicroStrategy Inc.ViennaUSA
  2. 2.The Pennsylvania State UniversityUSA

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