Multi-criteria classification – A new scheme for application of dominance-based decision rules

Błaszczyński, Jerzy; Greco, Salvatore; Słowiński, Roman

doi:10.1016/j.ejor.2006.03.004

Cited by 183 publications

(74 citation statements)

References 10 publications

(22 reference statements)

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“…If x P ≤0 2 y and x P ≥0 2 y and x P ≤0 3 y and x P ≥0 3 y, then x Sy or x S c y; (1,1), (1,3), (1,7), (2,2), (2,6), (2,8), (3,1), (3,3), (3,7), (4,4), (4,5), (5,4), (5,5), (6,2), (6,6), (6,8), (7,1), (7,3), (7,7), (8,2), (8,6), (8,8) If x P ≤−1 2 y and x P ≥1 3 y, then x Sy or x S c y; (2,4), (2,5), (6,4), (6,…”

Section: If X P ≥1unclassified

“…1 y and x P ≥1 2 y, then x Sy; (1,6), (3,6), (7,6) If x P ≥1 2 y and x P ≥0 3 y, then x Sy; (1,2), (1,6), (1,8), (3,2), (3,6), (3,8), (7,2), (7,6), (7,8) If If x P ≥0 2 y and x P ≥1 3 y, then x Sy; (1,4), (1,5), (3,4), (3,5), (7,4), (7,5) If y and x P ≤0 3 y, then x S c y; (2,1),(2,7),(6,1), (6,3), (6,7), (8,1), (8,7) Moreover, it is possible to induce five minimal D ≥≤ -decision rules from the boundary of approximation of S and S c :…”

Section: If X P ≥1mentioning

confidence: 99%

“…A new classification algorithm has been proposed in (Blaszczynski, Greco & Slowinski, 2007). Let ϕ 1 → ψ 1 , .…”

Section: Rule-based Classification Algorithmsmentioning

confidence: 99%

“…The C-lower approximations and the C-upper approximations of S and S c , obtained by means of multigraded dominance relations, are: (1,4), (1,5), (1,6), (1,8), (3,2), (3,4), (3,5), (3,6), (3,8), (7,2), (7,4), (7,5), (7,6), (7,8) C(S c ) = (2,1), (2,7), (4,1), (4,3), (4,7), (5,1), (5,3), (5,7), (6,1), (6,3), (6,7), (8,1), (8,7) All the remaining 36 pairs of reference objects belong to the C-boundaries of S and S c , i.e. Bn C (S) = Bn C (S c ).…”

Section: Moreover R E D S Pc T = C O R E S Pcmentioning

confidence: 99%

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Rough set and rule-based multicriteria decision aiding

2012

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ABSTRACT. 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.

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Section: If X P ≥1unclassified

Section: If X P ≥1mentioning

confidence: 99%

“…A new classification algorithm has been proposed in (Blaszczynski, Greco & Slowinski, 2007). Let ϕ 1 → ψ 1 , .…”

Section: Rule-based Classification Algorithmsmentioning

confidence: 99%

Section: Moreover R E D S Pc T = C O R E S Pcmentioning

confidence: 99%

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Rough set and rule-based multicriteria decision aiding

2012

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“…Though Pawlak's rough set theory has been demonstrated to be useful in the fields such as knowledge discover [3,4], decision analysis [5,6], data mining [7,8], pattern recognition [9], artificial intelligence [10], medical diagnosis [11] and so on.…”

Section: Introductionmentioning

confidence: 99%

Incomplete Variable Multigranulation Rough Sets Decision

Zhang¹,

Wang²,

Yang³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Recently, a multigranulation rough set (MGRS) has become a new direction in rough set theory, which is different from the Pawlake's rough set since the former takes multiple granulations on the universe into account. In this paper, by analyzing the limitations of optimistic multigranulation rough set (OMGRS) and pessimistic multigranulation rough set (PMGRS) in incomplete information system, the incomplete variable multigranulation rough set (VMGRS) is proposed, and the relationships among VMGRS, OMGRS and PMGRS are deeply explored, From the properties, it can be found that OMGRS and PMGRS are the special cases compared to our VMGRS. Furthermore, several important measurements are introduced into the VMGRS; it is shown that the measurements of the VMGRS are between the measurements of OMGRS and PMGRS. Finally, some numerical examples are employed to substantiate the conceptual arguments.

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