get_family

This function identifies and returns the family for a given node in a Bayesian network.
The family is a set of nodes, which includes the node itself, its parents, and the class.

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

get_family function

<dl><dt>node</dt>
<dd>A node in the bayesian network.</dd>
<dt>df</dt>
<dd>An adjacency list representing the network, formatted as a data frame.</dd>
<dt>class.str</dt>
<dd>The name of the class node.</dd></dl>

Arguments

Retrieve Families for a Given Node in Bayesian Network — get_family

<dl>

<dt>node</dt>
<dd>A node in the bayesian network.</dd>


<dt>df</dt>
<dd>An adjacency list representing the network, formatted as a data frame.</dd>


<dt>class.str</dt>
<dd>The name of the class node.</dd>

</dl>

get_family: Retrieve Families for a Given Node in Bayesian Network

Description

Usage

Value

Arguments