InformationGainHeuristic

<p>Performs the feature-clustering using entropy-based filters.</p>

manip

cluster

Provides a novel framework to able to automatically develop and deploy
an accurate Multiple Classifier System based on the feature-clustering
distribution achieved from an input dataset. 'D2MCS' was developed focused on
four main aspects: (i) the ability to determine an effective method to
evaluate the independence of features, (ii) the identification of the
optimal number of feature clusters, (iii) the training and tuning of ML
models and (iv) the execution of voting schemes to combine the outputs of
each classifier comprising the Multiple  Classifier System.

Miguel Ferreiro-D<c3><ad>az

D2MCS

Data Driving Multiple Classifier System

David Ruano-Ordás

Miguel Ferreiro-Díaz

José Ramón Méndez

University of Vigo 

InformationGainHeuristic function

<p><code>D2MCS::GenericHeuristic</code> -&gt; <code>InformationGainHeuristic</code></p>

Super class


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/InformationGainHeuristic%24new()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::InformationGainHeuristic$new()"><code>InformationGainHeuristic$new()</code></a></p></li>
<li><p><a href="/link/InformationGainHeuristic%24heuristic()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::InformationGainHeuristic$heuristic()"><code>InformationGainHeuristic$heuristic()</code></a></p></li>
<li><p><a href="/link/InformationGainHeuristic%24clone()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::InformationGainHeuristic$clone()"><code>InformationGainHeuristic$clone()</code></a></p></li>
</ul></p><p></p></div><p><hr>
<a id="method-InformationGainHeuristic-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>Empty function used to initialize the object arguments in
runtime.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>InformationGainHeuristic$new()</code></pre><p></p></div><p></p>
</div><p></p><p>
</p><p><hr>
<a id="method-InformationGainHeuristic-heuristic"></a></p><div class="section" id="method-heuristic-">
<h3>Method <code>heuristic()</code></h3>
<p>The algorithm find weights of discrete attributes basing on
their correlation with continuous class attribute. Particularly
Information Gain uses <code>H(Class) + H(Attribute) - H(Class, Attribute)</code></p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>InformationGainHeuristic$heuristic(col1, col2, column.names = NULL)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>col1</code></dt>
<dd><p>A <a href="/link/numeric?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::numeric">numeric</a> vector or matrix required to perform the
clustering operation.</p></dd></dl></div></p><p>
<dt><code>col2</code></dt>
<dd><p>A <a href="/link/numeric?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::numeric">numeric</a> vector or matrix to perform the clustering
operation.</p></dd></p><p>
<dt><code>column.names</code></dt>
<dd><p>an optional <a href="/link/character?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::character">character</a> vector with the names of
both columns.</p></dd></p><p>
</p><p></p>

<div class="section" id="returns">
<h4>Returns</h4>
<p>A <a href="/link/numeric?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::numeric">numeric</a> vector of length 1 or <a href="/link/NA?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::NA">NA</a> if an error
occurs.</p>
</div><p></p><p></p><p><hr>
<a id="method-InformationGainHeuristic-clone"></a></p><div class="section" id="method-clone-">
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>InformationGainHeuristic$clone(deep = FALSE)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd></dl></div></p><p>
</p><p></p>
<p></p><p>
</p>

Methods

Feature-clustering based on InformationGain methodology. — InformationGainHeuristic


<div class='section' id='public-methods'>
<h3>Public methods</h3>

<ul>
<li><p><a href='#method-InformationGainHeuristic-new'><code>InformationGainHeuristic$new()</code></a></p></li>
<li><p><a href='#method-InformationGainHeuristic-heuristic'><code>InformationGainHeuristic$heuristic()</code></a></p></li>
<li><p><a href='#method-InformationGainHeuristic-clone'><code>InformationGainHeuristic$clone()</code></a></p></li>
</ul>

</div><p><hr>
<a id="method-InformationGainHeuristic-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>Empty function used to initialize the object arguments in
runtime.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>InformationGainHeuristic$new()</code></pre><p></div></p>
</div>


</div><p><hr>
<a id="method-InformationGainHeuristic-heuristic"></a></p><div class='section' id='method-heuristic-'>
<h3>Method <code>heuristic()</code></h3>
<p>The algorithm find weights of discrete attributes basing on
their correlation with continuous class attribute. Particularly
Information Gain uses <code>H(Class) + H(Attribute) - H(Class, Attribute)</code></p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>InformationGainHeuristic$heuristic(col1, col2, column.names = NULL)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>col1</code></dt>
<dd><p>A <a href='https://rdrr.io/r/base/numeric.html'>numeric</a> vector or matrix required to perform the
clustering operation.</p></dd>


<dt><code>col2</code></dt>
<dd><p>A <a href='https://rdrr.io/r/base/numeric.html'>numeric</a> vector or matrix to perform the clustering
operation.</p></dd>


<dt><code>column.names</code></dt>
<dd><p>an optional <a href='https://rdrr.io/r/base/character.html'>character</a> vector with the names of
both columns.</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='returns'>
<h4>Returns</h4>
<p>A <a href='https://rdrr.io/r/base/numeric.html'>numeric</a> vector of length 1 or <a href='https://rdrr.io/r/base/NA.html'>NA</a> if an error
occurs.</p>
</div>

</div><p><hr>
<a id="method-InformationGainHeuristic-clone"></a></p><div class='section' id='method-clone-'>
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>InformationGainHeuristic$clone(deep = FALSE)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd>


</dl><p></div></p>
</div>

</div>


InformationGainHeuristic: Feature-clustering based on InformationGain methodology.

Description

Arguments

Super class

Methods

Public methods

Method `new()`

Usage

Method `heuristic()`

Usage

Arguments

Returns

Method `clone()`

Usage

Arguments

See Also

Description

Arguments

Super class

Methods

Public methods

Method new()

Usage

Method heuristic()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

See Also

Method `new()`

Method `heuristic()`

Method `clone()`