BC.LU.approximation.FRST: The fuzzy lower and upper approximations based on fuzzy rough...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/BasicFuzzyRoughSets.R

Description

This is a function implementing a fundamental concept of FRST: fuzzy lower and upper approximations. Many options have been considered for determining lower and upper approximations, such as techniques based on implicator and t-norm functions proposed by (Radzikowska and Kerre, 2002).

Usage

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BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
  type.LU = "implicator.tnorm", control = list())

Arguments

decision.table

a "DecisionTable" class representing the decision table. See SF.asDecisionTable.

IND.condAttr

a "IndiscernibilityRelation" class of the conditional attributes which is produced by BC.IND.relation.FRST.

IND.decAttr

a "IndiscernibilityRelation" class of the decision attribute which is produced by BC.IND.relation.FRST.

type.LU

a string representing a chosen method to calculate lower and upper approximations. See the explanation in Section Details.

control

a list of other parameters. In order to understand how to express the control parameter, see the explanation in Section Details. The descriptions of those components and their values is as follows.

  • t.tnorm: a type of triangular functions which have been explained

    in BC.IND.relation.FRST.

  • t.implicator: a type of implicator functions. The following are values of this parameter:

    • "kleene_dienes" means max(1 - x_1, x_2).

    • "lukasiewicz" means min(1 - x_1 + x_2, 1). It is the default value.

    • "zadeh" means max(1 - x_1, min(x_1, x_2)).

    • "gaines" means (x_1 <= x_2 ? 1 : x_2 / x_1).

    • "godel" means (x_1 <= x_2 ? 1 : x_2).

    • "kleene_dienes_lukasiewicz" means 1 - x_1 + x_1 * x_2.

    • "mizumoto" means (1 - x_1 + x_1 * x_2).

    • "dubois_prade" means (x_2 == 0 ? 1 - x_1 : (x_1 == 1 ? x_2 : 1)).

    Where we consider the following rule: x_1 -> x_2.

  • q.some: a vector of alpha and beta parameters of vaguely quantified rough set for quantifier some. The default value is q.some = c(0.1, 0.6).

  • q.most: a vector of alpha and beta parameters of vaguely quantified rough set for quantifier most. The default value is q.most = c(0.2, 1).

  • alpha: a numeric between 0 and 1 representing the threshold parameter of the fuzzy variable precision rough sets (FVPRS) (see Section Details). The default value is 0.05.

  • m.owa: an integer number (m) which is used in the OWA fuzzy rough sets (see Section Details).

    The default value is m.owa = round(0.5 * ncol(decision.table)).

  • w.owa: a vector representing the weight vector in the OWA fuzzy rough sets (see Section Details). The default value is NULL, which means we use the m.owa type.

  • type.rfrs: a type of robust fuzzy rough sets which is one of the following methods: "k.trimmed.min", "k.mean.min", "k.median.min", "k.trimmed.max", "k.mean.max", and "k.median.max" (see Section Details). The default value is "k.trimmed.min".

  • k.rfrs: a number between 0 and the number of data which is used to define considered data on robust fuzzy rough sets (RFRS) (see Section Details). The default value is k.rfrs = round(0.5*nrow(decision.table)).

  • beta.quasi: a number between 0 and 1 representing β-precision t-norms and t-conorms in β-PFRS. The default value is 0.05.

Details

Fuzzy lower and upper approximations as explained in B.Introduction-FuzzyRoughSets are used to define to what extent the set of elements can be classified into a certain class strongly or weakly. We can perform various methods by choosing the parameter type.LU. The following is a list of all type.LU values:

The parameter type.LU, which is explained above, is related with parameter control. In other words, when choosing a specific value of type.LU, we should take into account to set values of related components in control. The components that are considered depend on what kind of lower and upper approximations are used. So, we do not need to assign all components for a particular approach but only components related with type.LU. The following is a list showing the components of each approaches.

The description of the components can be seen in the control parameter. In Section Examples, we provide two examples showing different cases which are when we have to handle a nominal decision attribute and a continuous one.

It should be noted that this function depends on BC.IND.relation.FRST which is a function used to calculate the fuzzy indiscernibility relation as input data. So, it is obvious that before performing this function, users must execute BC.IND.relation.FRST first.

Value

A class "LowerUpperApproximation" representing fuzzy rough set (fuzzy lower and upper approximations). It contains the following components:

Author(s)

Lala Septem Riza

References

A. M. Radzikowska and E. E. Kerre, "A Comparative Study of Fuzzy Rough Sets", Fuzzy Sets and Systems, vol. 126, p. 137 - 156 (2002).

C. Cornelis, M. De Cock, and A. Radzikowska, "Vaguely Quantified Rough Sets", Proceedings of 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC2007), Lecture Notes in Artificial Intelligence 4482, p. 87 - 94 (2007).

C. Cornelis, N. Verbiest, and R. Jensen, "Ordered Weighted Average Based Fuzzy Rough Sets", Proceedings of the 5th International Conference on Rough Sets and Knowledge Technology (RSKT 2010), p. 78 - 85 (2010).

J. M. F. Salido and S. Murakami, "Rough Set Analysis of a General Type of Fuzzy Data Using Transitive Aggregations of Fuzzy Similarity Relations", Fuzzy Sets Syst., vol. 139, p. 635 - 660 (2003).

Q. Hu, L. Zhang, S. An, D. Zhang, and D. Yu, "On Robust Fuzzy Rough Set Models", IEEE Trans. on Fuzzy Systems, vol. 20, no. 4, p. 636 - 651 (2012).

R. Jensen and Q. Shen, "New Approaches to Fuzzy-Rough Feature Selection", IEEE Trans. on Fuzzy Systems, vol. 19, no. 4, p. 824 - 838 (2009).

R. R. Yager, "On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision Making", IEEE Transactions on Systems, Man, and Cybernetics, vol. 18, p. 183 - 190 (1988).

S. Y. Zhao, E. C. C. Tsang, and D. G. Chen, "The Model of Fuzzy Variable Precision Rough Sets", IEEE Trans. Fuzzy Systems, vol. 17, no. 2, p. 451 - 467 (2009).

See Also

BC.IND.relation.RST, BC.LU.approximation.RST, and BC.positive.reg.FRST

Examples

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###########################################################
## 1. Example: Decision table contains nominal decision attribute
## we are using the same dataset and indiscernibility
## relation along this example.
###########################################################
dt.ex1 <- 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.ex1) <- c("a", "b", "c", "d")
decision.table <- SF.asDecisionTable(dataset = dt.ex1, decision.attr = 4)

## let us consider the first and second attributes
## only as conditional attributes
condAttr <- c(1, 2)

## let us consider the fourth attribute as decision attribute
decAttr <- c(4)

#### calculate fuzzy indiscernibility relation ####
control.ind <- list(type.aggregation = c("t.tnorm", "lukasiewicz"),
                    type.relation = c("tolerance", "eq.1"))
control.dec <- list(type.aggregation = c("crisp"), type.relation = "crisp")

## fuzzy indiscernibility relation of conditional attribute
IND.condAttr <- BC.IND.relation.FRST(decision.table, attributes = condAttr,
                                     control = control.ind)

## fuzzy indiscernibility relation of decision attribute
IND.decAttr <- BC.IND.relation.FRST(decision.table, attributes = decAttr,
                                     control = control.dec)

#### Calculate fuzzy lower and upper approximation using type.LU : "implicator.tnorm" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz")
FRST.LU <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "implicator.tnorm", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU : "vqrs" ####
control <- list(q.some = c(0.1, 0.6), q.most = c(0.2, 1), t.tnorm = "lukasiewicz")
FRST.VQRS <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "vqrs", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU : "owa" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz", m.owa = 3)
FRST.OWA.1 <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "owa", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU :
#### "owa" with customized function
#### In this case, we are using the same weight vector as
#### previous one with m.owa = 3
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz",
               w.owa =  c(0, 0, 0, 0.14, 0.29, 0.57))
FRST.OWA.2 <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "owa", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU : "fvprs" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz", alpha = 0.05)
FRST.fvprs <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "fvprs", control = control)


#### Calculate fuzzy lower and upper approximation using type.LU : "rfrs" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz",
                type.rfrs = "k.trimmed.min", k.rfrs = 0)
FRST.rfrs <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "rfrs", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU : "beta.pfrs" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz", beta.quasi = 0.05)
FRST.beta.pfrs <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "beta.pfrs", control = control)

#### Calculate fuzzy lower and upper approximation using type.LU : "custom" ####
## In this case, we calculate approximations randomly.
f.lower <- function(x){
        return(min(runif(1, min = 0, max = 1) * x))
}
f.upper <- function(x){
        return(max(runif(1, min = 0, max = 1) * x))
}
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz", FUN.lower = f.lower,
                FUN.upper = f.upper)
FRST.custom <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "custom", control = control)

#### In this case, we use custom function for triangular norm and implicator operator
## For example, let us define our implicator and t-norm operator as follows.
imp.lower <- function(antecedent, consequent){
	                 return(max(1 - antecedent, consequent))
              }
tnorm.upper <- function(x, y){
                return (x * y)
             }
control.custom <- list(t.implicator = imp.lower, t.tnorm = tnorm.upper)
FRST.LU.custom <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "implicator.tnorm", control = control.custom)

###########################################################
## 2. Example: Decision table contains a continuous decision attribute.
## It should be noted that in this example, we are using
## the same dataset and indiscernibility relation.
## We only show one method but for other approaches
## the procedure is analogous to the previous example
###########################################################
## In this case, we are using housing dataset containing 7 objects
data(RoughSetData)
decision.table <- RoughSetData$housing7.dt

## let us consider the first and second conditional attributes only,
## and the decision attribute at 14.
cond.attributes <- c(1, 2)
dec.attributes <- c(14)
control.ind <- list(type.aggregation = c("t.tnorm", "lukasiewicz"),
               type.relation = c("tolerance", "eq.1"))
IND.condAttr <- BC.IND.relation.FRST(decision.table, attributes = cond.attributes,
                                     control = control.ind)
IND.decAttr <- BC.IND.relation.FRST(decision.table, attributes = dec.attributes,
                                    control = control.ind)

#### Calculate fuzzy lower and upper approximation using type.LU : "implicator.tnorm" ####
control <- list(t.implicator = "lukasiewicz", t.tnorm = "lukasiewicz")
FRST.LU <- BC.LU.approximation.FRST(decision.table, IND.condAttr, IND.decAttr,
              type.LU = "implicator.tnorm", control = control)

Example output

Loading required package: Rcpp

RoughSets documentation built on May 29, 2017, 7:06 p.m.