Not So Naive Bayes: Aggregating One-Dependence Estimators

By Geoffrey I. Webb, Janice R. Boughton, Zhihai Wang. Machine-mediated learning, 2005

723 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bbbc0facfbcd5788434f9ef7ff6ce47d95f7a3c1

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In Artificial Intelligence

Citations in the corpus

Learning from positive and unlabeled data: a survey (2018, methodology): “Averaged One-Dependence Estimator (AODE) [100] has been extended to PAODE, Hidden Naive Bayes (HNB) [42] to PHNB, and Full Bayesian network Classifier (FBC) [97] to PFBC [33].”
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers (2020, background): “…proposed in [7] where authors implemented the Linear Discriminant Analysis (LDA) [8], Quadratic Discriminant Analysis (QDA) [9], Naive Bayes (NB) [10], Gaussian Process Classiﬁcation (GPC) [11], Support Vector Machine (SVM) [12], Artiﬁcial Neural Network (ANN) [13], AdaBoost (AB) [14], Logistic…”
Machine learning for Internet of Things data analysis: A survey (2017, methodology): “Moreover, they are a popular model for applications, such as spam filtering [97], text categorization, and automatic medical diagnosis [98].”
Data Mining Static Code Attributes to Learn Defect Predictors (2007, positive): “have proposed an improvement to naive Bayes that aggregates 1-dependence estimators [49].”
A naive Bayes probability estimation model based on self-adaptive differential evolution (2014, methodology): “…Naive Bayes ·Probability estimation ·M-estimate ·Laplace-estimate · Differential evolution ·Self-adaptive ·Classification J. Wu · Z. Cai (B) School of Computer Science, China University of Geosciences, Wuhan 430074, People’s Republic of China e-mail: zhcai@cug.edu.cn J. Wu e-mail: wujiawb@126.com”
Deep feature weighting for naive Bayes and its application to text classification (2016, background): “On the other hand, there are many model averaging approaches proposed to improve naive Bayes (Dash and Cooper, 2002, 2004; Webb et al., 2005; Jiang et al., 2012a).”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “We compared the results obtained with the HNB classiﬁer with those obtained with the traditional Naïve Bayes classiﬁer and other extended Naïve Bayes models, including TAN, AODE, WAODE, NBTree, DTNB and the KDD’99 winner, which were used as a common benchmark in similar studies.”
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect (2012, methodology): “Models tested included Näıve Bayes [20], HNB (Hidden Näıve Bayes) [25], AODE (Aggregating One-Dependence Estimators) [26], Bayesian Networks [20], Multi-layer Perceptron neural networks [20], Random Forests [27], J48 Decision Trees (a variant of the classic C4.”
A Novel Bayes Model: Hidden Naive Bayes (2009, methodology): “presented an algorithm called averaged one-dependence estimators, simply AODE [12].”