scholarly journals Exact Learning Augmented Naive Bayes Classifier

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1703
Author(s):  
Shouta Sugahara ◽  
Maomi Ueno
Keyword(s):  
Naive Bayes ◽  
Marginal Likelihood ◽  
Large Data ◽  
Naïve Bayes ◽  
Superior Performance ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Exact Learning ◽  
Class Variable

Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.

scholarly journals Penerapan Metode Naive Bayes Classifier (Nbc) Untuk Klasifikasi Kondisi Internal Program Studi

Jurnal Teknik ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 65-74
Author(s):  
Indhitya Padiku
Keyword(s):  
Naive Bayes ◽  
Good Method ◽  
Naïve Bayes ◽  
Superior Performance ◽  
Misclassification Rate ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
Study Program

The low interest of prospective students in study programs at universities could be influenced by internal factors within the study program. These factors become the main variables in assessing the condition of the study program. For this reason, it is necessary to classify the internal conditions of the study program. A good method is needed in terms of accuracy and minimal misclassification to obtain the final classification results of the assessment. The purpose of this research is to classify the internal conditions of the study program. Classification of the internal conditions of the study program was carried out using the Naive Bayes Classifier (NBC) method which is a simple form of Bayesian Network with the assumption that all features are independent of each other. The NBC method shows an overall superior performance in terms of accuracy and misclassification rate. The NBC method can be used to determine the internal conditions of the study program, which could help identify factors that need to be addressed to increase the interest of prospective students enrolling in the study program.

Application of Naive Bayes Classifier for Information Extraction

2020 ◽  
Author(s):  
Nikhil Ranjan Nayak
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Naive Bayes ◽  
Large Data ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Information Need ◽  
Naïve Bayes Classifier ◽  
Access To Books

Information retrieval (IR) is the activity of obtaining information resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; stores and manages those documents. Web Search Engines are the most visible IR applications.It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

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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