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

This function computes rule base coverage, i.e. a an average of maximum membership degree at which each row of data fires the rules in rule base.

1 2 3 4 5 6 | ```
rbcoverage(
x,
rules,
tnorm = c("goedel", "goguen", "lukasiewicz"),
onlyAnte = TRUE
)
``` |

`x` |
Data for the rules to be evaluated on. Could be either a numeric
matrix or numeric vector. If matrix is given then the rules are evaluated
on rows. Each value of the vector or column of the matrix represents a
predicate - it's numeric value represents the truth values (values in the
interval |

`rules` |
Either an object of class "farules" or list of character
vectors where each vector is a rule with consequent being the first element
of the vector. Elements of the vectors (predicate names) must correspond to
the |

`tnorm` |
A character string representing a triangular norm to be used
(either |

`onlyAnte` |
TRUE if only antecedent-part of a rule should be evaluated. Antecedent-part of a rule are all predicates in rule vector starting from the 2nd position. (First element of a rule is the consequent - see above.) If FALSE, then the whole rule will be evaluated (antecedent part together with consequent). |

Let *f_{ij}* be a truth value of *i*-th rule on *j*-th row of
data `x`

. Then *m_j = max(f_{.j})* is a maximum truth value that
is reached for the *j*-th data row with the rule base. Then the rule
base coverage is a mean of that truth values, i.e. *rbcoverage =
mean(m_.)*.

A numeric value of the rule base coverage of given data.

Michal Burda

M. Burda, M. Štěpnička, Reduction of Fuzzy Rule Bases Driven by the Coverage of Training Data, in: Proc. 16th World Congress of the International Fuzzy Systems Association and 9th Conference of the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT 2015), Advances in Intelligent Systems Research, Atlantic Press, Gijon, 2015.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

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