Quality Assessment of Affymetrix GeneChip Data using the EM Algorithm and a Naive Bayes Classifier

2007 ◽  
Author(s):  
Brian E. Howard ◽  
Beate Sick ◽  
Imara Perera ◽  
Yang Ju Im ◽  
Heike Winter-Sederoff ◽  
...  
Keyword(s):  
Em Algorithm ◽  
Quality Assessment ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Affymetrix Genechip ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Genechip Data ◽  
The Em Algorithm

scholarly journals Determination of near-fault impulsive signals with multivariate naïve Bayes method

2021 ◽  
Author(s):  
Deniz Ertuncay ◽  
Giovanni Costa
Keyword(s):  
Naive Bayes ◽  
Strong Motion ◽  
Naïve Bayes ◽  
Strike Slip ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Earthquake Physics ◽  
Near Fault ◽  
A Site

AbstractNear-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

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