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Keywords
(7)
Classification Accuracy
Classification Error
Empirical Study
Monte Carlo Simulation
Mutual Information
Naive Bayes
Naive Bayes Classifier
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An empirical study of the naive Bayes classifier
An empirical study of the naive Bayes classifier,I. Rish
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An empirical study of the naive Bayes classifier
(
Citations: 149
)
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I. Rish
The
naive Bayes classifier
greatly simplify learn ing by assuming that features are independent given class. Although independence is generally a poor assumption, in practice
naive Bayes
often competes well with more sophisticated classifiers. Our broad goal is to understand the data character istics which affect the performance of naive Bayes. Our approach uses
Monte Carlo
simulations that al low a systematic study of
classification accuracy
for several classes of randomly generated prob lems. We analyze the impact of the distribution entropy on the classification error, showing that lowentropy feature distributions yield good per formance of naive Bayes. We also demonstrate that
naive Bayes
works well for certain nearly functional feature dependencies, thus reaching its best performance in two opposite cases: completely independent features (as expected) and function ally dependent features (which is surprising). An other surprising result is that the accuracy of
naive Bayes
is not directly correlated with the degree of feature dependencies measured as the class conditional
mutual information
between the fea tures. Instead, a better predictor of
naive Bayes
ac curacy is the amount of information about the class that is lost because of the independence assump tion.
Published in 2001.
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Citation Context
(112)
...We use the socalled Naive Bayes classifier based on univariate discrete distributions [
24
]...
Lars Kai Hansen
,
et al.
Good Friends, Bad News  Affect and Virality in Twitter
...Such a topology has proven to be quite effective in getting good results [
8
]...
A. C. van den Broek
,
et al.
Improving maritime situational awareness by fusing sensor information ...
...(1) Na¨ Bayes: The Na¨ Bayes classifier found its way into many applications nowadays due to its simple principle but yet powerful accuracy [
13
]...
Ahmed Lamkanfi
,
et al.
Comparing Mining Algorithms for Predicting the Severity of a Reported ...
...Naive Bayes approach is computationally efficient, and despite the fact that the naiveness assumptions are often not true, this classifier has worked well in many complex realworld situations [
25
]...
ChinAnn Yang
,
et al.
Automated detection of Focal Cortical Dysplasia lesions on T1weighted...
...Three algorithms including Support Vector Machine (SVM)[19], [20], Multilayer Perceptron (MLP) [21] and Naive Bayesian (NB) [22], [
23
] are tested and their performance is compared...
...3) Naive Bayes: Naive Bayes [22], [
23
] is a probabilistic classifier that applies Bayes’s theorem...
Bei Li
,
et al.
Predicting user comfort level using machine learning for Smart Grid en...
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Citations
(149)
Good Friends, Bad News  Affect and Virality in Twitter
Lars Kai Hansen
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