X.entropy

a distribution of values on the
  decision attribute.

decisionDistrib

It is an auxiliary function for <code>qualityF</code> parameter
  in the <code><a href="/link/FS.greedy.heuristic.reduct.RST?package=RoughSets&version=1.0-0" rd-options="" data-mini-rdoc="RoughSets::FS.greedy.heuristic.reduct.RST">FS.greedy.heuristic.reduct.RST</a></code> and
  <code><a href="/link/FS.greedy.heuristic.superreduct.RST?package=RoughSets&version=1.0-0" rd-options="" data-mini-rdoc="RoughSets::FS.greedy.heuristic.superreduct.RST">FS.greedy.heuristic.superreduct.RST</a></code>
  functions. It is based on <em>entropy</em> as a measure of
  information(C. E. Shannon, 1948).

This package provides comprehensive implementations of the rough
    set theory (RST) and the fuzzy rough set theory (FRST), and integrates
    these two theories into a single package. It provides implementations, not
    only for the basic concepts of RST and FRST, but also most common methods
    based on them for handling some tasks such as discretization, feature
    selection, instance selection, rule induction, and prediction. RST was
    introduced by Zdzislaw 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.entropy: The information gain measure function

Description

Usage

Arguments

Value

References