Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Improvement of CB & BC Algorithms (CB* Algorithm) for Learning Structure of Bayesian Networks as Classifier in Data Mining

Journal of ICT Research and Applications (JICTRA) · Vol. 1 No. 1 (2013) · pp. 29–41

DOI
10.5614/itbj.ict.2007.1.1.3
ISSN
2338-5499, 2337-5787
Published
2013-09-13
Publisher
DRI - ITB
Language
eng

Abstract

There are two categories of well-known approach (as basic principle of classification process) for learning structure of Bayesian Network (BN) in data mining (DM): scoring-based and constraint-based algorithms. Inspired by those approaches, we present a new CB* algorithm that is developed by considering four related algorithms: K2, PC, CB, and BC. The improvement obtained by our algorithm is derived from the strength of its primitives in the process of learning structure of BN. Specifically, CB* algorithm is appropriate for incomplete databases (having missing value), and without any prior information about node ordering.

Collaboration network of the authors

19 authors · 33 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • HAN University of Applied Sciences
  • Parahyangan Catholic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bayu HendradjayaBandung Institute of Technology3
Fransiskus AdikaraBandung Institute of Technology2
Masayu Leylia KhodraBandung Institute of Technology2
Nasy`an Taufiq Al GhifariBandung Institute of Technology2
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Dwi Hendratmo WidyantoroBandung Institute of Technology1
Fazat Nur AzizahBandung Institute of Technology1
Guido P. BakemaHAN University of Applied Sciences1
Oerip S. SantosoBandung Institute of Technology1
Fitra ArifiansyahBandung Institute of Technology1
Muhammad Zuhri Catur CandraBandung Institute of Technology1
Veronica Sri MoertiniParahyangan Catholic University1
Oerip S. SantosaAffiliation unknown1
Regina Yulia YasminBandung Institute of Technology1
Putri SaptawatiBandung Institute of Technology1
Mohamad Syafri TuloliBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Improvement of CB &amp; BC Algorithms (CB* Algorithm) for Learning Structure of Bayesian Networks as Classifier in Data Mining</dc:title>
	<dc:creator>Sitohang, Benhard</dc:creator>
	<dc:creator>Putri Saptawati, G. A.</dc:creator>
	<dc:description xml:lang="en-US">There are two categories of well-known approach (as basic principle of classification process) for learning structure of Bayesian Network (BN) in data mining (DM): scoring-based and constraint-based algorithms. Inspired by those approaches, we present a new CB* algorithm that is developed by considering four related algorithms: K2, PC, CB, and BC. The improvement obtained by our algorithm is derived from the strength of its primitives in the process of learning structure of BN. Specifically, CB* algorithm is appropriate for incomplete databases (having missing value), and without any prior information about node ordering.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2013-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/164</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.2007.1.1.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 1 No. 1 (2007); 29-41</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/164/158</dc:relation>
</oai_dc:dc>