BC.discernibility.mat.FRST: The decision-relative discernibility matrix based on fuzzy...
In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
View source: R/BasicFuzzyRoughSets.R
BC.discernibility.mat.FRST R Documentation
The decision-relative discernibility matrix based on fuzzy rough set theory
Description
This is a function that is used to build the decision-relative discernibility matrix based on FRST.
It is a matrix whose elements contain discernible attributes among pairs of objects.
By means of this matrix, we are able to produce all decision reducts of the given decision system.
Usage
BC.discernibility.mat.FRST(
decision.table,
type.discernibility = "standard.red",
control = list()
)
Arguments
decision.table
a "DecisionTable" class representing the decision table. See SF.asDecisionTable.
It should be noted that this case only supports the nominal/symbolic decision attribute.
type.discernibility
a string representing a type of discernibility. See in Section Details.
control
a list of other parameters.
-
type.relation: a type of fuzzy indiscernibility relation. The default value is type.relation = c("tolerance", "eq.1").
See BC.IND.relation.FRST.
-
type.aggregation: a type of aggregation operator. The default value is type.aggregation = c("t.tnorm", "lukasiewicz").
See BC.IND.relation.FRST.
-
t.implicator: a type of implicator operator. The default value is "lukasiewicz".
See BC.LU.approximation.FRST.
-
type.LU: a type of method of lower and upper approximations. The default value is "implicator.tnorm".
See BC.LU.approximation.FRST.
-
alpha.precision: a numeric value representing a precision variable. It is used when using "alpha.red" as type.discernibility.
The default value is 0.05.
See BC.LU.approximation.FRST.
-
show.discernibilityMatrix: a boolean value determining whether the discernibility matrix will be shown or not (NULL). The default value is FALSE.
-
epsilon: a numeric between 0 and 1 representing the \epsilon value on
type.discernibility = "gaussian.red". It should be noted that when having nominal values on all attributes then \epsilon should be 0.
The default value is 0.
-
delta: a numeric representing the \delta value on "gaussian" equations
(see BC.IND.relation.FRST). The default value is 2.
-
range.object: a vector representing considered objects to construct the k-relative discernibility matrix.
The default value is NULL which means that we are using all objects in the decision table.
Details
In this function, we provide several approaches in order to generate the decision-relative discernibility matrix.
Theoretically, all reducts are found by constructing
the matrix that contains elements showing discernible attributes among objects.
The discernible attributes are determined by a specific condition which depends on the selected algorithm. A particular approach can be executed by selecting
a value of the parameter type.discernibility. The following shows the different
values of the parameter type.discernibility corresponding approaches considered in this function.
-
"standard.red": It is adopted from (Tsang et al, 2008)'s approach.
The concept has been explained briefly in Introduction-FuzzyRoughSets.
In order to use this algorithm, we assign the control parameter
with the following components:
control = list(type.aggregation, type.relation, type.LU, t.implicator)
The detailed description of the components can be seen in BC.IND.relation.FRST and
BC.LU.approximation.FRST. Furthermore, in this case the authors suggest to use the "min" t-norm
(i.e., type.aggregation = c("t.tnorm", "min")) and the implicator operator "kleene_dienes" (i.e., t.implicator = "kleene_dienes").
-
"alpha.red": It is based on (Zhao et al, 2009)'s approach where all reductions will
be found by building an \alpha-discernibility matrix. This matrix contains elements which are defined by
1) if x_i and x_j belong to different decision concept,
c_{ij} = \{R : \mathcal{T}(R(x_i, x_j), \lambda) \le \alpha \},
where \lambda = (R_{\alpha} \downarrow A)(u) which is lower approximation
of FVPRS (See BC.LU.approximation.FRST).
2) c_{ij}={\oslash}, otherwise.
To generate the discernibility matrix based on this approach, we use the control parameter
with the following components:
control = list(type.aggregation, type.relation, t.implicator, alpha.precision)
where the lower approximation \lambda is fixed to type.LU = "fvprs". The detailed
description of the components can be seen in BC.IND.relation.FRST and BC.LU.approximation.FRST.
Furthermore, in this case the authors suggest to use \mathcal{T}-similarity relation
e.g., type.relation = c("transitive.closure", "eq.3"),
the "lukasiewicz" t-norm (i.e., type.aggregation = c("t.tnorm", "lukasiewicz")), and alpha.precision from 0 to 0.5.
-
"gaussian.red": It is based on (Chen et al, 2011)'s approach. The discernibility matrix contains elements which are defined by:
1) if x_i and x_j belong to different decision concept,
c_{ij}= \{R : R(x_i, x_j) \le \sqrt{1 - \lambda^2(x_i)}\},
where \lambda = inf_{u \in U}\mathcal{I}_{cos}(R(x_i, u), A(u)) - \epsilon. To generate fuzzy relation R , we use the fixed parameters as follows:
t.tnorm = "t.cos" and type.relation = c("transitive.kernel", "gaussian").
2) c_{ij}={\oslash}, otherwise.
In this case, we need to define control parameter as follows.
control <- list(epsilon)
It should be noted that when having nominal values on all attributes then epsilon (\epsilon) should be 0.
-
"min.element": It is based on (Chen et al, 2012)'s approach where we only consider finding
the minimal element of the discernibility matrix by introducing the binary relation DIS(R) the relative discernibility relation
of conditional attribute R with respect to decision attribute d, which is computed as
DIS(R) = \{(x_i, x_j) \in U \times U: 1 - R(x_i, x_j) > \lambda_i, x_j \notin [x_i]_d\},
where \lambda_i = (Sim(R) \downarrow [x_i]_d)(x_i) with Sim(R) a fuzzy equivalence relation.
In other words, this algorithm does not need to build the discernibility matrix.
To generate the fuzzy relation R and lower approximation \lambda, we use the control parameter
with the following components:
control = list(type.aggregation, type.relation, type.LU, t.implicator).
The detailed description of the components can be seen in BC.IND.relation.FRST and
BC.LU.approximation.FRST.
Value
A class "DiscernibilityMatrix" containing the following components:
-
disc.mat: a matrix showing the decision-relative discernibility matrix M(\mathcal{A})
which contains n \times n where n is the number of objects. It will be printed when choosing
show.discernibilityMatrix = TRUE.
-
disc.list: the decision-relative discernibility represented in a list.
-
discernibility.type: a string showing the chosen type of discernibility methods.
-
type.model: in this case, it is "FRST".
Author(s)
Lala Septem Riza
References
D. Chen, L. Zhang, S. Zhao, Q. Hu, and P. Zhu, "A Novel Algorithm for Finding Reducts
with Fuzzy Rough Sets", IEEE Trans. on Fuzzy Systems, vol. 20, no. 2, p. 385 - 389 (2012).
D. G. Chen, Q. H. Hu, and Y. P. Yang, "Parameterized Attribute Reduction with
Gaussian Kernel Based Fuzzy Rough Sets", Information Sciences, vol. 181, no. 23,
p. 5169 - 5179 (2011).
E. C. C. Tsang, D. G. Chen, D. S. Yeung, X. Z. Wang, and J. W. T. Lee,
"Attributes Reduction Using Fuzzy Rough Sets", IEEE Trans. Fuzzy Syst.,
vol. 16, no. 5, p. 1130 - 1141 (2008).
S. Zhao, E. C. C. Tsang, and D. Chen, "The Model of Fuzzy Variable Precision Rough Sets",
IEEE Trans. on Fuzzy Systems, vol. 17, no. 2, p. 451 - 467 (2009).
See Also
BC.discernibility.mat.RST, BC.LU.approximation.RST, and BC.LU.approximation.FRST
Examples
#######################################################################
## Example 1: Constructing the decision-relative discernibility matrix
## In this case, we are using The simple Pima dataset containing 7 rows.
#######################################################################
data(RoughSetData)
decision.table <- RoughSetData$pima7.dt
## using "standard.red"
control.1 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "min"),
t.implicator = "kleene_dienes", type.LU = "implicator.tnorm")
res.1 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
control = control.1)
## using "gaussian.red"
control.2 <- list(epsilon = 0)
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "gaussian.red",
control = control.2)
## using "alpha.red"
control.3 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "min"),
t.implicator = "lukasiewicz", alpha.precision = 0.05)
res.3 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "alpha.red",
control = control.3)
## using "min.element"
control.4 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "lukasiewicz"),
t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.4 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "min.element",
control = control.4)
#######################################################################
## Example 2: Constructing the decision-relative discernibility matrix
## In this case, we are using the Hiring dataset containing nominal values
#######################################################################
## Not run: data(RoughSetData)
decision.table <- RoughSetData$hiring.dt
control.1 <- list(type.relation = c("crisp"),
type.aggregation = c("crisp"),
t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.1 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
control = control.1)
control.2 <- list(epsilon = 0)
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "gaussian.red",
control = control.2)
## End(Not run)
RoughSets documentation built on May 29, 2024, 7:34 a.m.
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View source: R/BasicFuzzyRoughSets.R
| BC.discernibility.mat.FRST | R Documentation |
The decision-relative discernibility matrix based on fuzzy rough set theory
Description
This is a function that is used to build the decision-relative discernibility matrix based on FRST. It is a matrix whose elements contain discernible attributes among pairs of objects. By means of this matrix, we are able to produce all decision reducts of the given decision system.
Usage
BC.discernibility.mat.FRST(
decision.table,
type.discernibility = "standard.red",
control = list()
)
Arguments
decision.table |
a |
type.discernibility |
a string representing a type of discernibility. See in Section |
control |
a list of other parameters.
|
Details
In this function, we provide several approaches in order to generate the decision-relative discernibility matrix.
Theoretically, all reducts are found by constructing
the matrix that contains elements showing discernible attributes among objects.
The discernible attributes are determined by a specific condition which depends on the selected algorithm. A particular approach can be executed by selecting
a value of the parameter type.discernibility. The following shows the different
values of the parameter type.discernibility corresponding approaches considered in this function.
-
"standard.red": It is adopted from (Tsang et al, 2008)'s approach. The concept has been explained briefly inIntroduction-FuzzyRoughSets. In order to use this algorithm, we assign thecontrolparameter with the following components:control = list(type.aggregation, type.relation, type.LU, t.implicator)The detailed description of the components can be seen in
BC.IND.relation.FRSTandBC.LU.approximation.FRST. Furthermore, in this case the authors suggest to use the "min" t-norm (i.e.,type.aggregation = c("t.tnorm", "min")) and the implicator operator "kleene_dienes" (i.e.,t.implicator = "kleene_dienes"). -
"alpha.red": It is based on (Zhao et al, 2009)'s approach where all reductions will be found by building an\alpha-discernibility matrix. This matrix contains elements which are defined by1) if
x_iandx_jbelong to different decision concept,c_{ij} = \{R : \mathcal{T}(R(x_i, x_j), \lambda) \le \alpha \},where
\lambda = (R_{\alpha} \downarrow A)(u)which is lower approximation of FVPRS (SeeBC.LU.approximation.FRST).2)
c_{ij}={\oslash}, otherwise.To generate the discernibility matrix based on this approach, we use the
controlparameter with the following components:control = list(type.aggregation, type.relation, t.implicator, alpha.precision)where the lower approximation
\lambdais fixed totype.LU = "fvprs". The detailed description of the components can be seen inBC.IND.relation.FRSTandBC.LU.approximation.FRST. Furthermore, in this case the authors suggest to use\mathcal{T}-similarity relatione.g.,
type.relation = c("transitive.closure", "eq.3"),the "lukasiewicz" t-norm (i.e.,
type.aggregation = c("t.tnorm", "lukasiewicz")), andalpha.precisionfrom 0 to 0.5. -
"gaussian.red": It is based on (Chen et al, 2011)'s approach. The discernibility matrix contains elements which are defined by:1) if
x_iandx_jbelong to different decision concept,c_{ij}= \{R : R(x_i, x_j) \le \sqrt{1 - \lambda^2(x_i)}\},where
\lambda = inf_{u \in U}\mathcal{I}_{cos}(R(x_i, u), A(u)) - \epsilon. To generate fuzzy relationR, we use the fixed parameters as follows:t.tnorm = "t.cos"andtype.relation = c("transitive.kernel", "gaussian").2)
c_{ij}={\oslash}, otherwise.In this case, we need to define
controlparameter as follows.control <- list(epsilon)It should be noted that when having nominal values on all attributes then
epsilon(\epsilon) should be 0. -
"min.element": It is based on (Chen et al, 2012)'s approach where we only consider finding the minimal element of the discernibility matrix by introducing the binary relationDIS(R)the relative discernibility relation of conditional attributeRwith respect to decision attributed, which is computed asDIS(R) = \{(x_i, x_j) \in U \times U: 1 - R(x_i, x_j) > \lambda_i, x_j \notin [x_i]_d\},where
\lambda_i = (Sim(R) \downarrow [x_i]_d)(x_i)withSim(R)a fuzzy equivalence relation. In other words, this algorithm does not need to build the discernibility matrix. To generate the fuzzy relationRand lower approximation\lambda, we use thecontrolparameter with the following components:control = list(type.aggregation, type.relation, type.LU, t.implicator).The detailed description of the components can be seen in
BC.IND.relation.FRSTandBC.LU.approximation.FRST.
Value
A class "DiscernibilityMatrix" containing the following components:
-
disc.mat: a matrix showing the decision-relative discernibility matrixM(\mathcal{A})which containsn \times nwherenis the number of objects. It will be printed when choosingshow.discernibilityMatrix = TRUE. -
disc.list: the decision-relative discernibility represented in a list. -
discernibility.type: a string showing the chosen type of discernibility methods. -
type.model: in this case, it is"FRST".
Author(s)
Lala Septem Riza
References
D. Chen, L. Zhang, S. Zhao, Q. Hu, and P. Zhu, "A Novel Algorithm for Finding Reducts with Fuzzy Rough Sets", IEEE Trans. on Fuzzy Systems, vol. 20, no. 2, p. 385 - 389 (2012).
D. G. Chen, Q. H. Hu, and Y. P. Yang, "Parameterized Attribute Reduction with Gaussian Kernel Based Fuzzy Rough Sets", Information Sciences, vol. 181, no. 23, p. 5169 - 5179 (2011).
E. C. C. Tsang, D. G. Chen, D. S. Yeung, X. Z. Wang, and J. W. T. Lee, "Attributes Reduction Using Fuzzy Rough Sets", IEEE Trans. Fuzzy Syst., vol. 16, no. 5, p. 1130 - 1141 (2008).
S. Zhao, E. C. C. Tsang, and D. Chen, "The Model of Fuzzy Variable Precision Rough Sets", IEEE Trans. on Fuzzy Systems, vol. 17, no. 2, p. 451 - 467 (2009).
See Also
BC.discernibility.mat.RST, BC.LU.approximation.RST, and BC.LU.approximation.FRST
Examples
#######################################################################
## Example 1: Constructing the decision-relative discernibility matrix
## In this case, we are using The simple Pima dataset containing 7 rows.
#######################################################################
data(RoughSetData)
decision.table <- RoughSetData$pima7.dt
## using "standard.red"
control.1 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "min"),
t.implicator = "kleene_dienes", type.LU = "implicator.tnorm")
res.1 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
control = control.1)
## using "gaussian.red"
control.2 <- list(epsilon = 0)
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "gaussian.red",
control = control.2)
## using "alpha.red"
control.3 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "min"),
t.implicator = "lukasiewicz", alpha.precision = 0.05)
res.3 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "alpha.red",
control = control.3)
## using "min.element"
control.4 <- list(type.relation = c("tolerance", "eq.1"),
type.aggregation = c("t.tnorm", "lukasiewicz"),
t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.4 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "min.element",
control = control.4)
#######################################################################
## Example 2: Constructing the decision-relative discernibility matrix
## In this case, we are using the Hiring dataset containing nominal values
#######################################################################
## Not run: data(RoughSetData)
decision.table <- RoughSetData$hiring.dt
control.1 <- list(type.relation = c("crisp"),
type.aggregation = c("crisp"),
t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.1 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
control = control.1)
control.2 <- list(epsilon = 0)
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "gaussian.red",
control = control.2)
## End(Not run)
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