X.laplace

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

rule

A function returning a weight of rule's vote understood as the Laplace estimate of its confidence.

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 based on indiscernibility relations to
    model and process imprecise or incomplete information. It works on
    symbolic-valued datasets for tackling the data analysis problems. 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.

X.laplace: Rule voting by the Laplace estimate

Description

Usage

Arguments

Value

See Also