Volume 4, Issue 1, February 2011, Pages 1 - 11
Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model
Huaxiong Li, Xianzhong Zhou
Received 12 June 2009, Accepted 9 July 2010, Available Online 1 February 2011.
- https://doi.org/10.2991/ijcis.2011.4.1.1How to use a DOI?
- Keywords: decision-theoretic rough set; three-way view decision; risk decision making; Bayesian decision
- Rough set theory has witnessed great success in data mining and knowledge discovery, which provides a good support for decision making on a certain data. However, a practical decision problem always shows diversity under the same circumstance according to different personality of the decision makers. A simplex decision model can not provide a full description on such diverse decisions. In this article, a review of Pawlak rough set models and probabilistic rough set models is presented, and a three-way view decision model based on decision-theoretic rough set model is proposed, in which optimistic decision, pessimistic decision, and equable decision are provided according to the cost of misclassification. The thresholds of probabilistic inclusion are calculated based on minimization of risk cost under respective decision bias. The study not only presents a new theoretic decision model considering the different personality of the decision makers, but also provides a practical explanation and an illustrative example on diverse risk bias decision. Keywords: decision-theoretic rough set; three-way view decision; risk decision making; Bayesian decision
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Huaxiong Li AU - Xianzhong Zhou PY - 2011 DA - 2011/02 TI - Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model JO - International Journal of Computational Intelligence Systems SP - 1 EP - 11 VL - 4 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2011.4.1.1 DO - https://doi.org/10.2991/ijcis.2011.4.1.1 ID - Li2011 ER -