A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty
Abstract
This paper represents a generic feature extraction approach to handle multiple attribute decision analysis problems. For that purpose, available decision support frameworks are carefully studied and the basic types of attributes involved in the decision problems are identified. Based on this analysis, a generic decision support scheme is proposed that can deal with all sorts of attributes in order to deduce the optimal solution for any decision problem. The proposed framework is capable of handling multiple attributes throughout the process of providing a flawless solution for the decision problem under both risk and uncertainty. This paper provides detailed information about the sources of uncertainty in the decision-making process and proposes a sophisticated approach for capturing all sorts of uncertainties. In the proposed approach, a cross assessment of every attribute against the corresponding attribute of the other alternatives is conducted to extract the significant features of an attribute. The relative importance of every attribute is considered as a supporting knowledge representation parameter in order to optimize the attribute-assessment process. The final decision is made based on the numerical scores seized by the alternatives. The paper also represents a numerical study to demonstrate the potential applications of the proposed methodology.
Keywords:
multi-attribute, decision problem, feature extraction, knowledge base, uncertainty, risk, cross evaluationDownloads
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