Approximate Bayesian network classifiers

被引：0

作者：

Slezak, D ^{[1
]}

Wróblewski, J ^{[1
]}

机构：

[1] Polish Japanese Inst Informat Technol, PL-02008 Warsaw, Poland

来源：

ROUGH SETS AND CURRENT TRENDS IN COMPUTING, PROCEEDINGS | 2002年 / 2475卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bayesian network (BN) is a directed acyclic graph encoding probabilistic independence statements between variables. BN with decision attribute as a root can be applied to classification of new cases, by synthesis of conditional probabilities propagated along the edges. We consider approximate BNs, which almost keep entropy of a decision table. They have usually less edges than classical BNs. They enable to model and extend the well-known Naive Bayes approach. Experiments show that classifiers based on approximate BNs can be very efficient.

引用

页码：365 / 372

页数：8

共 50 条

[41] Bayesian network classifiers for mineral potential mapping
Porwal, A
Carranza, EJM
Hale, M
[J]. COMPUTERS & GEOSCIENCES, 2006, 32 (01) : 1 - 16
[42] Decision Boundary for Discrete Bayesian Network Classifiers
Varando, Gherardo
Bielza, Concha
Larranaga, Pedro
[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2015, 16 : 2725 - 2749
[43] On discriminative Bayesian network classifiers and logistic regression
Roos T.
Wettig H.
Grünwald P.
Myllymäki P.
Tirri H.
[J]. Machine Learning, 2005, 59 (03) : 267 - 296
[44] On Discriminative Parameter Learning of Bayesian Network Classifiers
Pernkopf, Franz
Wohlmayr, Michael
[J]. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT II, 2009, 5782 : 221 - 237
[45] Adaptive learning algorithms for Bayesian network classifiers
Departamento de Matemática, CEOC, Universidade de Aveiro, Aveiro 3810-193, Portugal
[J]. AI Commun, 2008, 1 (87-88):
[46] A Symbolic Approach to Explaining Bayesian Network Classifiers
Shih, Andy
Choi, Arthur
Darwiche, Adnan
[J]. PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2018, : 5103 - 5111
[47] Efficient parameter learning of Bayesian network classifiers
Nayyar A. Zaidi
Geoffrey I. Webb
Mark J. Carman
François Petitjean
Wray Buntine
Mike Hynes
Hans De Sterck
[J]. Machine Learning, 2017, 106 : 1289 - 1329
[48] Learning continuous time Bayesian network classifiers
Codecasa, Daniele
Stella, Fabio
[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2014, 55 (08) : 1728 - 1746
[49] Face detection by aggregated Bayesian network classifiers
Pham, TV
Worring, M
Smeulders, AWM
[J]. MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, 2001, 2123 : 249 - 262
[50] Decision boundary for discrete Bayesian network classifiers
[J]. 2015, Microtome Publishing (16)

← 1 2 3 4 5 →