StrLearningPBIL_BNM: Structure Learning for BNM by PBIL

In exametrika: Test Theory Analysis and Biclustering

Structure Learning for BNM by PBIL

Description

Generating a DAG from data using a Population-Based Incremental Learning

Usage

StrLearningPBIL_BNM(
  U,
  Z = NULL,
  w = NULL,
  na = NULL,
  seed = 123,
  population = 20,
  Rs = 0.5,
  Rm = 0.002,
  maxParents = 2,
  maxGeneration = 100,
  successiveLimit = 5,
  elitism = 0,
  alpha = 0.05,
  estimate = 1,
  filename = NULL,
  verbose = TRUE
)

Arguments

U	U is either a data class of exametrika, or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function.
Z	Z is a missing indicator matrix of the type matrix or data.frame
w	w is item weight vector
na	na argument specifies the numbers or characters to be treated as missing values.
seed	seed for random.
population	Population size. The default is 20
Rs	Survival Rate. The default is 0.5
Rm	Mutation Rate. The default is 0.002
maxParents	Maximum number of edges emanating from a single node. The default is 2.
maxGeneration	Maximum number of generations.
successiveLimit	Termination conditions. If the optimal individual does not change for this number of generations, it is considered to have converged.
elitism	Number of elites that remain without crossover when transitioning to the next generation.
alpha	Learning rate. The default is 0.05
estimate	In PBIL for estimating the adjacency matrix, specify by number from the following four methods: 1. Optimal adjacency matrix, 2. Rounded average of individuals in the last generation, 3. Rounded average of survivors in the last generation, 4. Rounded generational gene of the last generation. The default is 1.
filename	Specify the filename when saving the generated adjacency matrix in CSV format. The default is null, and no output is written to the file.
verbose	verbose output Flag. default is TRUE

Details

This function performs structural learning using the Population-Based Incremental Learning model(PBIL) proposed by Fukuda et al.(2014) within the genetic algorithm framework. Instead of learning the adjacency matrix itself, the 'genes of genes' that generate the adjacency matrix are updated with each generation. For more details, please refer to Fukuda(2014) and Section 8.5.2 of the text(Shojima,2022).

Value

adj

Optimal adjacency matrix

testlength

Length of the test. The number of items included in the test.

TestFitIndices

Overall fit index for the test.See also TestFit

nobs

Sample size. The number of rows in the dataset.

testlength

Length of the test. The number of items included in the test.

crr

correct response ratio

TestFitIndices

Overall fit index for the test.See also TestFit

param

Learned Parameters

CCRR_table

Correct Response Rate tables

References

Fukuda, S., Yamanaka, Y., & Yoshihiro, T. (2014). A Probability-based evolutionary algorithm with mutations to learn Bayesian networks. International Journal of Artificial Intelligence and Interactive Multimedia, 3, 7–13. DOI: 10.9781/ijimai.2014.311

Examples

# Perform Structure Learning for Bayesian Network Model using PBIL
# (Population-Based Incremental Learning)
StrLearningPBIL_BNM(J5S10,
  population = 20, # Size of population in each generation
  Rs = 0.5, # 50% survival rate for next generation
  Rm = 0.005, # 0.5% mutation rate for genetic diversity
  maxParents = 2, # Maximum of 2 parent nodes per item
  alpha = 0.05, # Learning rate for probability update
  estimate = 4 # Use rounded generational gene method
)

exametrika documentation built on Aug. 21, 2025, 5:27 p.m.