21,092 research outputs found
Class-based Rough Approximation with Dominance Principle
Dominance-based Rough Set Approach (DRSA), as the extension of Pawlak's Rough
Set theory, is effective and fundamentally important in Multiple Criteria
Decision Analysis (MCDA). In previous DRSA models, the definitions of the upper
and lower approximations are preserving the class unions rather than the
singleton class. In this paper, we propose a new Class-based Rough
Approximation with respect to a series of previous DRSA models, including
Classical DRSA model, VC-DRSA model and VP-DRSA model. In addition, the new
class-based reducts are investigated.Comment: Submitted to IEEE-GrC201
Aggregation procedure based on majority principle for collective identification of firm’s crucial knowledge
It is very important to identify, preserve, and transfer knowledge to those who need it within firm. However, the identification of knowledge and especially tacit knowledge is a complex process because knowledge cannot be measured quantitatively. In this paper we present an approach for inducing a set of collective decision rules representing a generalized description of the preferential information of a group of decision makers involved in a multicriteria classification problem to identify crucial knowledge to be capitalized
Rough set and rule-based multicriteria decision aiding
The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems
Generalizing GAMETH: Inference rule procedure..
In this paper we present a generalisation of GAMETH framework, that play an important role in identifying crucial knowledge. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Knowledge Management; Knowledge Capitalizing; Managing knowledge; crucial knowledge;
Extension of the fuzzy dominance-based rough set approach using ordered weighted average operators
In the article we rst review some known results on fuzzy versions of the dominance-based rough set approach (DRSA) where we expand the theory considering additional properties. Also, we apply Ordinal Weighted Average (OWA) operators in fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables when it is combined with the indiscernibility-based rough set approach (IRSA).We examine theoretical properties of the proposed combination with fuzzy DRSA
Development of Stresses in Cohesionless Poured Sand
The pressure distribution beneath a conical sandpile, created by pouring sand
from a point source onto a rough rigid support, shows a pronounced minimum
below the apex (`the dip'). Recent work of the authors has attempted to explain
this phenomenon by invoking local rules for stress propagation that depend on
the local geometry, and hence on the construction history, of the medium. We
discuss the fundamental difference between such approaches, which lead to
hyperbolic differential equations, and elastoplastic models, for which the
equations are elliptic within any elastic zones present .... This displacement
field appears to be either ill-defined, or defined relative to a reference
state whose physical existence is in doubt. Insofar as their predictions depend
on physical factors unknown and outside experimental control, such
elastoplastic models predict that the observations should be intrinsically
irreproducible .... Our hyperbolic models are based instead on a physical
picture of the material, in which (a) the load is supported by a skeletal
network of force chains ("stress paths") whose geometry depends on construction
history; (b) this network is `fragile' or marginally stable, in a sense that we
define. .... We point out that our hyperbolic models can nonetheless be
reconciled with elastoplastic ideas by taking the limit of an extremely
anisotropic yield condition.Comment: 25 pages, latex RS.tex with rspublic.sty, 7 figures in Rsfig.ps.
Philosophical Transactions A, Royal Society, submitted 02/9
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