Naive Bayesian Classifiers for Ranking

Machine Learning: ECML 2004 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-30115-8_46 ◽

2004 ◽

pp. 501-512 ◽

Cited By ~ 33

Author(s):

Harry Zhang ◽

Jiang Su

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Alternative prior assumptions for improving the performance of naïve Bayesian classifiers

Data Mining and Knowledge Discovery ◽

10.1007/s10618-008-0101-6 ◽

2008 ◽

Vol 18 (2) ◽

pp. 183-213 ◽

Cited By ~ 23

Author(s):

Tzu-Tsung Wong

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers

Journal of Chemical Information and Modeling ◽

10.1021/ci050374h ◽

2006 ◽

Vol 46 (1) ◽

pp. 193-200 ◽

Cited By ~ 85

Author(s):

Meir Glick ◽

Jeremy L. Jenkins ◽

James H. Nettles ◽

Hamilton Hitchings ◽

John W. Davies

Keyword(s):

Support Vector Machines ◽

High Throughput ◽

High Throughput Screening ◽

Recursive Partitioning ◽

Support Vector ◽

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Vector Machines

On the Use of Naive Bayesian Classifiers for Detecting Elementary and Coordinated Attacks

Fundamenta Informaticae ◽

10.3233/fi-2010-373 ◽

2010 ◽

Vol 105 (4) ◽

pp. 435-466 ◽

Cited By ~ 3

Author(s):

Tayeb Kenaza ◽

Karim Tabia ◽

Salem Benferhat

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

A Method to Boost Naïve Bayesian Classifiers

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/3-540-47887-6_11 ◽

2002 ◽

pp. 115-122 ◽

Cited By ~ 2

Author(s):

Lili Diao ◽

Keyun Hu ◽

Yuchang Lu ◽

Chunyi Shi

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Publishing naive Bayesian classifiers

Proceedings of the VLDB Endowment ◽

10.14778/1687627.1687759 ◽

2009 ◽

Vol 2 (1) ◽

pp. 1174-1185 ◽

Cited By ~ 4

Author(s):

Barzan Mozafari ◽

Carlo Zaniolo

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification

Applied Soft Computing ◽

10.1016/j.asoc.2016.12.043 ◽

2017 ◽

Vol 54 ◽

pp. 183-199 ◽

Cited By ~ 43

Author(s):

Diab M. Diab ◽

Khalil M. El Hindi

Keyword(s):

Differential Evolution ◽

Text Classification ◽

Fine Tuning ◽

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Extending Naive Bayesian Classifiers with continuous attributes using tree-like Graphical Models

Journal of Convergence Information Technology ◽

10.4156/jcit.vol7.issue10.48 ◽

2012 ◽

Vol 7 (10) ◽

pp. 405-411

Author(s):

Du Ruijie ◽

Wang Shuangcheng ◽

Wang Hanxing ◽

Yu Minjie

Keyword(s):

Graphical Models ◽

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian

Correlation Analysis in Classifiers

Advances in Data Mining and Database Management - Data Mining in Public and Private Sectors ◽

10.4018/978-1-60566-906-9.ch011 ◽

2010 ◽

pp. 204-218

Author(s):

Vincent Lemaire ◽

Carine Hue ◽

Olivier Bernier

Keyword(s):

Correlation Analysis ◽

General Framework ◽

New Method ◽

Classification Model ◽

Data Sets ◽

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Predictive Probability ◽

Input Variables

This chapter presents a new method to analyze the link between the probabilities produced by a classification model and the variation of its input values. The goal is to increase the predictive probability of a given class by exploring the possible values of the input variables taken independently. The proposed method is presented in a general framework, and then detailed for naive Bayesian classifiers. We also demonstrate the importance of “lever variables”, variables which can conceivably be acted upon to obtain specific results as represented by class probabilities, and consequently can be the target of specific policies. The application of the proposed method to several data sets shows that such an approach can lead to useful indicators.

A hybrid discretization method for naïve Bayesian classifiers

Pattern Recognition ◽

10.1016/j.patcog.2011.12.014 ◽

2012 ◽

Vol 45 (6) ◽

pp. 2321-2325 ◽

Cited By ~ 27

Author(s):

Tzu-Tsung Wong

Keyword(s):

Discretization Method ◽

Bayesian Classifiers ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Hybrid Discretization

Individual attribute prior setting methods for naïve Bayesian classifiers

Pattern Recognition ◽

10.1016/j.patcog.2010.11.002 ◽

2011 ◽

Vol 44 (5) ◽

pp. 1041-1047 ◽

Cited By ~ 8

Author(s):

Tzu-Tsung Wong ◽

Liang-Hao Chang

Keyword(s):

Bayesian Classifiers ◽

Naive Bayesian ◽

Individual Attribute ◽

Naïve Bayesian