NDLI: A Novel Bayes Model: Hidden Naive Bayes

Content Provider	IEEE Xplore Digital Library
Author	Liangxiao Jiang Zhang, H. Zhihua Cai
Copyright Year	1989
Abstract	Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB). In HNB, a hidden parent is created for each attribute which combines the influences from all other attributes. We experimentally test HNB in terms of classification accuracy, using the 36 UCI data sets selected by Weka, and compare it to naive Bayes (NB), selective Bayesian classifiers (SBC), naive Bayes tree (NBTree), tree-augmented naive Bayes (TAN), and averaged one-dependence estimators (AODE). The experimental results show that HNB significantly outperforms NB, SBC, NBTree, TAN, and AODE. In many data mining applications, an accurate class probability estimation and ranking are also desirable. We study the class probability estimation and ranking performance, measured by conditional log likelihood (CLL) and the area under the ROC curve (AUC), respectively, of naive Bayes and its improved models, such as SBC, NBTree, TAN, and AODE, and then compare HNB to them in terms of CLL and AUC. Our experiments show that HNB also significantly outperforms all of them.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	1361
Ending Page	1371
Page Count	11
File Size	4916964
File Format	PDF
ISSN	10414347
Volume Number	21
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Classification tree analysis Niobium NP-hard problem Testing Data mining Area measurement Classification algorithms Computational complexity ranking. Naive Bayes Bayesian network classifiers learning algorithms classification class probability estimation
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A novel Naive Bayes model: Packaged Hidden Naive Bayes

Learning lazy naive Bayesian classifiers for ranking

Naive Bayesian classifiers for ranking (2004)

Learning instance greedily cloning naive Bayes for ranking

Tractable Bayesian Learning of Tree Augmented Naive Bayes Classifiers (2003)

Bayesian Network Classifiers. (1997)

Building Classifiers using Bayesian Networks (1996)

Tractable bayesian learning of tree augmented naive bayes classifiers (2003)

Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination

A Novel Bayes Model: Hidden Naive Bayes

Similar Documents

A novel Naive Bayes model: Packaged Hidden Naive Bayes

Learning lazy naive Bayesian classifiers for ranking

Naive Bayesian classifiers for ranking (2004)

Learning instance greedily cloning naive Bayes for ranking

Tractable Bayesian Learning of Tree Augmented Naive Bayes Classifiers (2003)

Bayesian Network Classifiers. (1997)

Building Classifiers using Bayesian Networks (1996)

Tractable bayesian learning of tree augmented naive bayes classifiers (2003)

Scaling Up the Accuracy of Decision-Tree Classifiers: A Naive-Bayes Combination

A Novel Bayes Model: Hidden Naive Bayes