This is a function used to implement a fundamental concept of FRST which is fuzzy indiscernibility relations.
It is used for any fuzzy relations that determine the degree to which two objects are indiscernibility.
The detailed description about basic concepts of FRST
can be found in B.IntroductionFuzzyRoughSets
.
1  BC.IND.relation.FRST(decision.table, attributes = NULL, control = list())

decision.table 
a 
attributes 
a numerical vector expressing indexes of subset of attributes to be considered.
The default value is 
control 
a list of other parameters consisting of the following parameters:

Briefly, the indiscernibility relation is a relation that shows a degree of similarity among the objects. For example, R(x_i, x_j) = 0 means the object x_i is completely different from x_j, and otherwise if R(x_i, x_j) = 1, while between those values we consider a degree of similarity. To calculate this relation, several methods have been implemented in this function which are approaches based on fuzzy tolerance, equivalence and Tequivalence relations. The fuzzy tolerance relations proposed by (Jensen and Shen, 2009) include three equations while (Hu, 2004) proposed five T_{cos}transitive kernel functions as fuzzy Tequivalence relations. The simple algorithm of fuzzy equivalence relation is implemented as well. Furthermore, we facilitate users to define their own equation for similarity relation.
To calculate a particular relation, we should pay attention to several components in
the parameter control
. The main component in the control
parameter is type.relation
that defines
what kind of approach we are going to use. The detailed explanation about the parameters and their equations
is as follows:
"tolerance"
: It refers to fuzzy tolerance relations proposed by (Jensen and Shen, 2009).
In order to represent the "tolerance"
relation, we must set type.relation
as follows:
type.relation = c("tolerance", <chosen equation>)
where the chosen equation called as t.similarity
is one of the
"eq.1"
, "eq.2"
, and "eq.3"
equations which have been explained in B.IntroductionFuzzyRoughSets
.
"transitive.kernel"
: It refers to the relations employing kernel functions (Genton, 2001).
In order to represent the relation, we must set the type.relation
parameter as follows.
type.relation = c("transitive.kernel", <chosen equation>, <delta>)
where the chosen equation is one of five following equations (called t.similarity
):
"gaussian"
: It means Gaussian kernel which is R_G(x,y) = \exp (\frac{x  y^2}{δ})
"exponential"
: It means exponential kernel which is R_E(x,y) = \exp(\frac{x  y}{δ})
"rational"
: It means rational quadratic kernel which is R_R(x,y) = 1  \frac{x  y^2}{x  y^2 + δ}
"circular"
: It means circular kernel which is if x  y < δ,
R_C(x,y) = \frac{2}{π}\arccos(\frac{x  y}{δ})  \frac{2}{π}\frac{x  y}{δ}√{1  (\frac{x  y}{δ})^2}
"spherical"
: It means spherical kernel which is if x  y < δ,
R_S(x,y) = 1  \frac{3}{2}\frac{x  y}{δ} + \frac{1}{2}(\frac{x  y}{δ})^3
and delta
is a specified value.
For example: let us assume we are going to use "transitive.kernel"
as the fuzzy relation,
"gaussian"
as its equation and the delta is 0.2. So, we assign the type.relation
parameter as follows:
type.relation = c("transitive.kernel", "gaussian", 0.2)
If we omit the delta
parameter then we are using "gaussian"
defined as R_E(x,y) = \exp(\frac{x  y}{2σ^2}), where σ is the variance.
Furthermore, when using this relation, usually we set
type.aggregation = c("t.tnorm", "t.cos")
.
"kernel.frst"
: It refers to Tequivalence relation proposed by (Hu, 2004).
In order to represent the relation, we must set type.relation
parameter as follows.
type.relation = c("kernel.frst", <chosen equation>, <delta>)
where the chosen equation is one of the kernel functions, but they have different names corresponding to previous ones:
"gaussian.kernel"
, "exponential.kernel"
, "rational.kernel"
, "circular.kernel"
, and "spherical.kernel"
. And
delta
is a specified value.
For example: let us assume we are going to use "gaussian.kernel"
as its equation and the delta is 0.2.
So, we assign the type.relation
parameter as follows:
type.relation = c("kernel.frst", "gaussian.kernel", 0.2)
In this case, we do not need to define type of aggregation. Furthemore, regarding the distance used in the equations if objects x and y contains mixed values (nominal and continuous) then we use the Gower distance and we use the euclidean distance for continuous only.
"transitive.closure"
: It refers to similarity relation (also called fuzzy equivalence relation).
We consider a simple algorithm to calculate this relation as follows:
Input: a fuzzy relation R
Output: a mintransitive fuzzy relation R^m
Algorithm:
1. For every x, y: compute
R'(x,y) = max(R(x,y), max_{z \in U}min(R(x,z), R(z,y)))
2. If R' \not= R, then R ≥ts R' and go to 1, else R^m ≥ts R'
For interested readers, other algorithms can be seen in (Naessens et al, 2002). Let "eq.1"
be the R fuzzy relations, to define it as parameter is
type.relation = c("transitive.closure", "eq.1")
. We can also use other equations that have been explained in "tolerance"
and "transitive.kernel"
.
"crisp"
: It uses crisp equality for all attributes and
we set the parameter type.relation = "crisp"
. In this case, we only have R(x_i, x_j) = 0
which means the object x_i is completely different from x_j,
and otherwise if R(x_i, x_j) = 1.
"custom"
: this value means that we define our own equation for the indiscernibility relation.
The equation should be defined in parameter FUN.relation
.
type.relation = c("custom", <FUN.relation>)
The function FUN.relation
should consist of three arguments which are decision.table
,
x
, and y
, where x
and y
represent two objects which we want to compare.
It should be noted that the user must ensure that the values of this equation are always between 0 and 1.
An example can be seen in Section Examples
.
Beside the above type.relation
, we provide several options of values for the type.aggregation
parameter.
The following is a description about it.
type.aggregation = c("crisp")
: It uses crisp equality for all attributes.
type.aggregation = c("t.tnorm", <t.tnorm operator>)
: It means we are using "t.tnorm"
aggregation
which is a triangular norm operator with
a specified operator t.tnorm
as follows:
"min"
: standard tnorm i.e., min(x_1, x_2).
"hamacher"
: hamacher product i.e., (x_1 * x_2)/(x_1 + x_2  x_1 * x_2).
"yager"
: yager class i.e., 1  min(1, ((1  x_1) + (1  x_2))).
"product"
: product tnorm i.e., (x_1 * x_2).
"lukasiewicz"
: lukasiewicz's tnorm (default) i.e., max(x_2 + x_1  1, 0).
"t.cos"
: T_{cos}tnorm i.e., max(x_1 * x_2  √{1  x_1^2}√{1  x_2^2, 0}).
FUN.tnorm
: It is a userdefined function for "t.tnorm"
. It has to have two arguments, for example:
FUN.tnorm < function(left.side, right.side)
if ((left.side + right.side) > 1)
return(min(left.side, right.side))
else return(0)
The default value is type.aggregation = c("t.tnorm", "lukasiewicz")
.
type.aggregation = c("custom", <FUN.agg>)
: It is used to define our own function for a type of aggregations. <FUN.agg>
is
a function having one argument representing data that is produced by fuzzy similarity equation calculation.
The data is a list of one or many matrices which depend on the number of considered attributes and has dimension:
the number of object \times the number of object. For example:
FUN.agg < function(data) return(Reduce("+", data)/length(data))
which is a function to calculate average along all attributes. Then,
we can set type.aggregation
as follows:
type.aggregation = c("general.custom", <FUN.agg>)
. An example can be seen in Section Examples
.
Furthermore, the use of this function has been illustrated in Section Examples
.
Finally, this function is important since it is a basic function needed by other functions, such as BC.LU.approximation.FRST
and
BC.positive.reg.FRST
for calculating lower and upper approximation and determining positive regions.
A class "IndiscernibilityRelation"
which contains
IND.relation
: a matrix representing the indiscernibility relation over all objects.
type.relation
: a vector representing the type of relation.
type.aggregation
: a vector representing the type of aggregation operator.
type.model
: a string showing the type of model which is used. In this case it is "FRST"
which means fuzzy rough set theory.
Lala Septem Riza
M. Genton, "Classes of Kernels for Machine Learning: a Statistics Perspective", J. Machine Learning Research, vol. 2, p. 299  312 (2001).
H. Naessens, H. De Meyer, and B. De Baets, "Algorithms for the Computation of TTransitive Closures", IEEE Trans. on Fuzzy Systems, vol. 10, No. 4, p. 541  551 (2002).
R. Jensen and Q. Shen, "New Approaches to FuzzyRough Feature Selection", IEEE Trans. on Fuzzy Systems, vol. 19, no. 4, p. 824  838 (2009).
Q. Hu, D. Yu, W. Pedrycz, and D. Chen, "Kernelized Fuzzy Rough Sets and Their Applications", IEEE Trans. Knowledge Data Eng., vol. 23, no. 11, p. 1649  1471 (2011).
BC.LU.approximation.FRST
, BC.IND.relation.RST
, BC.LU.approximation.RST
,
and BC.positive.reg.FRST
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110  ###########################################################
## Example 1: Dataset containing nominal values for
## all attributes.
###########################################################
## Decision table is represented as data frame
dt.ex1 < data.frame(c(1,0,2,1,1,2,2,0), c(0, 1,0, 1,0,2,1,1),
c(2,1,0,0,2,0,1,1), c(2,1,1,2,0,1,1,0), c(0,2,1,2,1,1,2,1))
colnames(dt.ex1) < c("aa", "bb", "cc", "dd", "ee")
decision.table < SF.asDecisionTable(dataset = dt.ex1, decision.attr = 5,
indx.nominal = c(1:5))
## In this case, we only consider the second and third attributes.
attributes < c(2, 3)
## calculate fuzzy indiscernibility relation ##
## in this case, we are using "crisp" as a type of relation and type of aggregation
control.ind < list(type.relation = c("crisp"), type.aggregation = c("crisp"))
IND < BC.IND.relation.FRST(decision.table, attributes = attributes, control = control.ind)
###########################################################
## Example 2: Dataset containing realvalued attributes
###########################################################
dt.ex2 < data.frame(c(0.4, 0.4, 0.3, 0.3, 0.2, 0.2),
c(0.3, 0.2, 0.4, 0.3, 0.3, 0),
c(0.5, 0.1, 0.3, 0, 0, 0),
c("no", "yes", "no", "yes", "yes", "no"))
colnames(dt.ex2) < c("a", "b", "c", "d")
decision.table < SF.asDecisionTable(dataset = dt.ex2, decision.attr = 4)
## in this case, we only consider the first and second attributes
attributes < c(1, 2)
## Calculate fuzzy indiscernibility relation
## in this case, we choose "tolerance" relation and "eq.1" as similarity equation
## and "lukasiewicz" as tnorm of type of aggregation
control.1 < list(type.aggregation = c("t.tnorm", "lukasiewicz"),
type.relation = c("tolerance", "eq.1"))
IND.1 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.1)
## Calculate fuzzy indiscernibility relation: transitive.kernel
control.2 < list(type.aggregation = c("t.tnorm", "t.cos"),
type.relation = c("transitive.kernel", "gaussian", 0.2))
IND.2 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.2)
## Calculate fuzzy indiscernibility relation: kernel.frst
control.3 < list(type.relation = c("kernel.frst", "gaussian.kernel", 0.2))
IND.3 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.3)
## calculate fuzzy transitive closure
control.4 < list(type.aggregation = c("t.tnorm", "lukasiewicz"),
type.relation = c("transitive.closure", "eq.1"))
IND.4 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.4)
## Calculate fuzzy indiscernibility relation: using userdefined relation
## The customized function should have three arguments which are : decision.table
## and object x, and y.
## This following example shows that we have an equation for similarity equation:
## 1  abs(x  y) where x and y are two objects that will be compared.
## In this case, we do not consider decision.table in calculation.
FUN.relation < function(decision.table, x, y) {
return(1  (abs(x  y)))
}
control.5 < list(type.aggregation = c("t.tnorm", "lukasiewicz"),
type.relation = c("custom", FUN.relation))
IND.5 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.5)
## In this case, we calculate aggregation as average of all objects
## by executing the Reduce function
FUN.average < function(data){
return(Reduce("+", data)/length(data))
}
control.6 < list(type.aggregation = c("custom", FUN.average),
type.relation = c("tolerance", "eq.1"))
IND.6 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.6)
## using userdefined tnorms
FUN.tnorm < function(left.side, right.side) {
if ((left.side + right.side) > 1)
return(min(left.side, right.side))
else return(0)}
control.7 < list(type.aggregation = c("t.tnorm", FUN.tnorm),
type.relation = c("tolerance", "eq.1"))
IND.7 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.7)
## Calculate fuzzy indiscernibility relation: kernel fuzzy rough set
control.8 < list(type.relation = c("kernel.frst", "gaussian.kernel", 0.2))
IND.8 < BC.IND.relation.FRST(decision.table, attributes = attributes,
control = control.8)
##################################################################
## Example 3: Dataset containing continuous and nominal attributes
## Note. we only consider type.relation = c("tolerance", "eq.1")
## but other approaches have the same way.
##################################################################
data(RoughSetData)
decision.table < RoughSetData$housing7.dt
## in this case, we only consider the attribute: 1, 2, 3, 4
attributes < c(1,2,3,4)
## Calculate fuzzy indiscernibility relation
control.9 < list(type.aggregation = c("t.tnorm", "lukasiewicz"),
type.relation = c("tolerance", "eq.1"))
IND.9 < BC.IND.relation.FRST(decision.table, attributes = attributes, control = control.9)

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