Displaying similar documents to “An incremental approach to obtaining attribute reduction for dynamic decision systems”

A model of decision with linguistic knowledge.

María Teresa Lamata Jiménez (1994)

Mathware and Soft Computing

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The aim of this paper is to develop a new aggregating method for the decision problem in which the possible values of rewards are known in linguistic terms. We show new operators for solving this problem, as well as the way in which OWA operators provide us with an adequate framework for representing the optimism degree of the decision maker in case we have no information about the real state.

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

Dinh, Vu Van, Giang, Nguyen Long (2013)

Serdica Journal of Computing

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A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based...

Multi-attribute evaluation with imprecise vector utility

Sixto Ríos-Insua, Alfonso Mateos (1996)

Revista de la Real Academia de Ciencias Exactas Físicas y Naturales

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We consider the multi-attribute decision making problem with incomplete information on the decision maker's preferences, given by an imprecise vector utility function. We introduce an approximation set to the utility efficient set which may be used to aid a decision maker in reaching a final compromise strategy. We provide sorne properties and an interactive procedure based on such approximation set.