Extending the Bnclassify R package: Bayesian network classifiers with continuous variables

Cheng, Shanshan (2020). Extending the Bnclassify R package: Bayesian network classifiers with continuous variables. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Extending the Bnclassify R package: Bayesian network classifiers with continuous variables
Author/s:
  • Cheng, Shanshan
Contributor/s:
  • Mihaljevic, Bojan
  • Larrañaga Múgica, Pedro
Item Type: Thesis (Master thesis)
Masters title: Ciencia de Datos
Date: July 2020
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Nowadays, most of the open-source software for Bayesian networks only handles discrete variables. This limitation makes it difficult to use Bayesian networks in real life. The original bnclassify package provides the code that deals with the Bayes multinomial network (BMN). This kind of Bayesian network is not capable of handling continuous variables, if a data set contains continuous variables, it will be discretized, therefore, a loss of information will be introduced. The goal of this work is to extend the package and make it able to deal with continuous variables. To avoid the loss, this work assumes all variables follow the Gaussian distribution, therefore, new functionalities are added to the package including structural learning, parameter learning, and prediction with conditional Gaussian network (CGN). In order to determine the correctness and the efficiency of the new implementations called predictCGNs and GaussianImplement, two experiments are designed in this work. The experimental results demonstrate the usability of the program and there is no inconsistency between the new implementations and Bayesian networks.

More information

Item ID: 63626
DC Identifier: http://oa.upm.es/63626/
OAI Identifier: oai:oa.upm.es:63626
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 07 Sep 2020 10:23
Last Modified: 07 Sep 2020 10:23
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