# reduce: Reduce the size of rule base In beerda/lfl: Linguistic Fuzzy Logic

## Description

From given rule base, select such set of rules that influence mostly the rule base coverage of the input data.

## Usage

 ```1 2 3 4 5 6 7 8``` ```reduce( x, rules, ratio, tnorm = c("goedel", "goguen", "lukasiewicz"), tconorm = c("goedel", "goguen", "lukasiewicz"), numThreads = 1 ) ```

## Arguments

 `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 [0, 1]). `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 `x`'s names (of columns if `x` is a matrix). `ratio` A percentage of rule base coverage that must be preserved. It must be a value within the [0, 1] interval. Value of 1 means that the rule base coverage of the result must be the same as coverage of input `rules`. A sensible value is e.g. 0.9. `tnorm` Which t-norm to use as a conjunction of antecedents. The default is `"goedel"`. `tconorm` Which t-norm to use as a disjunction, i.e. to combine multiple antecedents to get coverage of the rule base. The default is `"goedel"`. `numThreads` How many threads to use for computation. Value higher than 1 causes that the algorithm runs in several parallel threads (using the OpenMP library).

## Details

From a given rulebase, a rule with greatest coverage is selected. After that, additional rules are selected that increase the rule base coverage the most. Addition stops after the coverage exceeds original coverage * ratio.

Note that the size of the resulting rule base is not necessarily minimal because the algorithm does not search all possible combination of rules. It only finds a local minimum of rule base size.

## Value

Function returns an instance of class `farules()` or a list depending on the type of the `rules` argument.

Michal Burda

## References

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.

`rbcoverage()`, `farules()`