MultinformationHeuristic

<p>Performs the feature-clustering using MCC score.
Valid for both bi-class and multi-class problems. Only valid for bi-class
problems.</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 

MultinformationHeuristic function

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

Super class


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/MultinformationHeuristic%24new()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::MultinformationHeuristic$new()"><code>MultinformationHeuristic$new()</code></a></p></li>
<li><p><a href="/link/MultinformationHeuristic%24heuristic()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::MultinformationHeuristic$heuristic()"><code>MultinformationHeuristic$heuristic()</code></a></p></li>
<li><p><a href="/link/MultinformationHeuristic%24clone()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::MultinformationHeuristic$clone()"><code>MultinformationHeuristic$clone()</code></a></p></li>
</ul></p><p></p></div><p><hr>
<a id="method-MultinformationHeuristic-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>MultinformationHeuristic$new()</code></pre><p></p></div><p></p>
</div><p></p><p>
</p><p><hr>
<a id="method-MultinformationHeuristic-heuristic"></a></p><div class="section" id="method-heuristic-">
<h3>Method <code>heuristic()</code></h3>
<p>Mutinformation takes two random variables as input and
computes the mutual information in nats according to the entropy
estimator method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>MultinformationHeuristic$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 vector/factor denoting a random variable or a data.frame
denoting a random vector where columns contain variables/features and
rows contain outcomes/samples.</p></dd></dl></div></p><p>
<dt><code>col2</code></dt>
<dd><p>An another random variable or random vector (vector/factor or
data.frame).</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>Returns the mutual information I(X;Y) in nats.</p>
</div><p></p><p></p><p><hr>
<a id="method-MultinformationHeuristic-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>MultinformationHeuristic$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 Mutual Information Computation theory. — MultinformationHeuristic


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

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

</div><p><hr>
<a id="method-MultinformationHeuristic-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>MultinformationHeuristic$new()</code></pre><p></div></p>
</div>


</div><p><hr>
<a id="method-MultinformationHeuristic-heuristic"></a></p><div class='section' id='method-heuristic-'>
<h3>Method <code>heuristic()</code></h3>
<p>Mutinformation takes two random variables as input and
computes the mutual information in nats according to the entropy
estimator method.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>MultinformationHeuristic$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 vector/factor denoting a random variable or a data.frame
denoting a random vector where columns contain variables/features and
rows contain outcomes/samples.</p></dd>


<dt><code>col2</code></dt>
<dd><p>An another random variable or random vector (vector/factor or
data.frame).</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>Returns the mutual information I(X;Y) in nats.</p>
</div>

</div><p><hr>
<a id="method-MultinformationHeuristic-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>MultinformationHeuristic$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>


MultinformationHeuristic: Feature-clustering based on Mutual Information Computation theory.

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()`