An empirical study of the naive Bayes classifier

By Irina Rish. 2001

1,115 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3cd1df035ce7a022eefaf3190627ffcd5e43eca3

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Citations in the corpus

Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation (2020, methodology): “According to the studies (Hand and Yu 2001; Domingos and Pazzani 1996; Zhang 2004; Rish 2001), an interesting yet an important outcome suggests that the conditional independence is only a sufﬁcient condition for the optimal of the NB, but not a necessary condition.”
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports (2012, methodology): “For the purpose of training and validation, the Naïve Bayes classifier [24] and the Maximum Entropy classifier [25] implemented within the MALLET toolkit [26] were applied with the default”
Using naive bayes to detect spammy names in social networks (2013, background): “, [11, 24, 21]) even though the value s(~x) of (2.”
Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning (2011, background): “The most important explanation for NB’s success, however, lies in the fact that conditional independence is only a sufficient but not a necessary condition for optimality (Domingos and Pazzani 1997; Hand and Yu 2001; Rish 2001;Zhang 2004;Zhang and Su 2004).”
Comparing different supervised machine learning algorithms for disease prediction (2019, background): “This classifier assumes that a particular feature in a class is not directly related to any other feature although features for that class could have interdependence among themselves [28].”
Convolutional neural network: a review of models, methodologies and applications to object detection (2019, methodology): “DL and deep convolution neural network (DCNN) have dramatically upgraded the performance beyond the state of the art in the above fields, compared to the conventional machine learning (ML) techniques, such as support vector machine (SVM) [3] and Naive Bayes [4].”
Are Financial Constraints Priced? Evidence from Textual Analysis (2018, unknown): “As has been well established in the computational linguistics literature, this relation is stable (McCallum and Nigam, 1998; Rish, 2001; Zhang, 2005).”
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone (2020, methodology): “[78]), and an ensemble boosting method (Gradient Boosting [79]).”
Cryptocurrency trading: a comprehensive survey (2020, methodology): “Naive Bayes (NB) [205], Support Vector Machine (SVM) [236], K-Nearest Neighbours (KNN) [236], Decision Tree (DT) [103], Random Forest (RF) [164] and Gradient Boosting (GB) [105] algorithms habe been used in cryptocurrency trading based on papers we collected.”
Predicting the severity of a reported bug (2010, methodology): “Even though the Naı̈ve Bayes classification algorithm is based on a simple probabilistic principle, this classifier has proven to perform quite good compared to more sophisticated algorithms [9].”
Good Friends, Bad News - Affect and Virality in Twitter (2011, methodology): “We use the so-called Naive Bayes classifier based on univariate discrete distributions [ 24 ].”