individual

Generates an individual (potential solution)

internal

Implements Differential Evolution (DE) to train parameters of Bayesian Networks for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given Bayesian Network structure encodes assumptions about conditional independencies among the attributes and will result in an error if they do not hold in the data. Such an error includes the classification dimension. The main goal of Discriminative learning is to minimize this type of error. This package provides main variants of differential evolution described in Price & Storn (1996) <doi:10.1109/ICEC.1996.542711> and recent ones, described in Tanabe & Fukunaga (2014) <doi:10.1109/CEC.2014.6900380> and Zhang & Sanderson (2009) <doi:10.1109/TEVC.2009.2014613> with adaptation mechanism for factor mutarion and crossover rate.

Alejandro Platas-L<c3><b3>pez

dplbnDE

Discriminative Parameter Learning of Bayesian Networks by
Differential Evolution

Alejandro Platas-Lopez

Alejandro Guerra-Hernandez

individual function

<dl><dt>W</dt>
<dd>Number of nodes in BN structure</dd>
<dt>X</dt>
<dd>Cardinality of CPTs</dd>
<dt>Y</dt>
<dd>Number of parameters per node</dd>
<dt>Z</dt>
<dd>BN Structure with CPT slots</dd></dl>

Arguments

Generates an individual (potential solution) — individual

<dl>

<dt>W</dt>
<dd>Number of nodes in BN structure</dd>


<dt>X</dt>
<dd>Cardinality of CPTs</dd>


<dt>Y</dt>
<dd>Number of parameters per node</dd>


<dt>Z</dt>
<dd>BN Structure with CPT slots</dd>

</dl>

individual: Generates an individual (potential solution)

Description

Usage

Arguments