X.rulesCounting

a decision rule, i.e. element of an "RuleSetRST" object

rule

A function returning an equal vote's weight for every rule. It corresponds to voting by counting the
matching rules.

Implementations of algorithms for data analysis based on the
rough set theory (RST) and the fuzzy rough set theory (FRST). We not only
provide implementations for the basic concepts of RST and FRST but also
popular algorithms that derive from those theories. The methods included in the
package can be divided into several categories based on their functionality:
discretization, feature selection, instance selection, rule induction and
classification based on nearest neighbors. RST was introduced by Zdzisław
Pawlak in 1982 as a sophisticated mathematical tool to model and process
imprecise or incomplete information. By using the indiscernibility relation for
objects/instances, RST does not require additional parameters to analyze the
data. FRST is an extension of RST. The FRST combines concepts of vagueness and
indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in
1965) and RST.

Christoph Bergmeir

RoughSets

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Andrzej Janusz

Lala Septem Riza

Dominik Ślęzak

Chris Cornelis

Francisco Herrera

Jose Manuel Benitez

Sebastian Stawicki

X.rulesCounting function

<dl><dt>rule</dt>
<dd>a decision rule, i.e. element of an "RuleSetRST" object</dd></dl>

Arguments

Author

Rule voting by counting matching rules — X.rulesCounting

<dl>

<dt>rule</dt>
<dd>a decision rule, i.e. element of an "RuleSetRST" object</dd>

</dl>

X.rulesCounting: Rule voting by counting matching rules

Description

Usage

Value

Arguments

Author

See Also