# FRBCS.W model building

### Description

This is the internal function that implements the fuzzy rule-based classification
system with weight factor (FRBCS.W). It is used to solve classification tasks.
Users do not need to call it directly,
but just use `frbs.learn`

and `predict`

. This method is
suitable only for classification problems.

### Usage

1 2 | ```
FRBCS.W(range.data, data.train, num.labels, num.class, type.mf,
type.tnorm = "MIN", type.snorm = "MAX", type.implication.func = "ZADEH")
``` |

### Arguments

`range.data` |
a matrix ( |

`data.train` |
a matrix ( |

`num.labels` |
a matrix ( |

`num.class` |
an integer number representing the number of labels (linguistic terms). |

`type.mf` |
the type of the shape of the membership functions. |

`type.tnorm` |
the type of t-norm. See |

`type.snorm` |
the type of s-norm. See |

`type.implication.func` |
the type of implication function. See |

### Details

This method is adopted from Ishibuchi and Nakashima's paper. Each fuzzy IF-THEN rule consists of antecedent linguistic values and a single consequent class with certainty grades (weights). The antecedent part is determined by a grid-type fuzzy partition from the training data. The consequent class is defined as the dominant class in the fuzzy subspace corresponding to the antecedent part of each fuzzy IF-THEN rule and the certainty grade is calculated from the ratio among the consequent class. A class of the new instance is determined by the consequent class of the rule with the maximal product of the compatibility grade and the certainty grade.

### References

H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rule-based classification systems", IEEE Transactions on Fuzzy Systems, vol. 1, pp. 59 - 64 (2001).

### See Also

`FRBCS.eng`

, `frbs.learn`

, and `predict`