A New Method of Online Fault Diagnosis Based on Incremental Continuous Attribute Naive Bayesian

2018 ◽  
Author(s):  
Mengting Li ◽  
Shuai Zhao ◽  
Shaowei Chen ◽  
Dengshan Huang
Keyword(s):  
Fault Diagnosis ◽  
New Method ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Continuous Attribute

AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES

2014 ◽  
Vol 28 (05) ◽  
pp. 1451007 ◽  
Author(s):  
CHANG-HWAN LEE
Keyword(s):  
Data Mining ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Feature Weighting ◽  
New Method ◽  
Filter Method ◽  
Information Theoretic ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Unrealistic Assumption

In spite of its simplicity, naive Bayesian learning has been widely used in many data mining applications. However, the unrealistic assumption that all features are equally important negatively impacts the performance of naive Bayesian learning. In this paper, we propose a new method that uses a Kullback–Leibler measure to calculate the weights of the features analyzed in naive Bayesian learning. Its performance is compared to that of other state-of-the-art methods over a number of datasets.

Correlation Analysis in Classifiers

2010 ◽  
pp. 204-218
Author(s):  
Vincent Lemaire ◽  
Carine Hue ◽  
Olivier Bernier
Keyword(s):  
Correlation Analysis ◽  
General Framework ◽  
New Method ◽  
Classification Model ◽  
Data Sets ◽  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Predictive Probability ◽  
Input Variables

This chapter presents a new method to analyze the link between the probabilities produced by a classification model and the variation of its input values. The goal is to increase the predictive probability of a given class by exploring the possible values of the input variables taken independently. The proposed method is presented in a general framework, and then detailed for naive Bayesian classifiers. We also demonstrate the importance of “lever variables”, variables which can conceivably be acted upon to obtain specific results as represented by class probabilities, and consequently can be the target of specific policies. The application of the proposed method to several data sets shows that such an approach can lead to useful indicators.

scholarly journals The Classification of Documents in Malay and Indonesian Using the Naive Bayesian Method Uses Words and Phrases as a Training Set

MENDEL ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 23-28
Author(s):  
Marvin Chandra Wijaya
Keyword(s):  
Classification Accuracy ◽  
Bayesian Method ◽  
New Method ◽  
Classification Method ◽  
Training Set ◽  
Naive Bayesian ◽  
Naïve Bayesian

Malay Language and Indonesian Language are two closely related languages, sharing a lot in common in the meanings of words and grammar. Classifying the two languages automatically using a tool is a challenge because the two languages are very similar. The classification method that is widely used today is the Naive Bayesian method. This method needs to be implemented in a particular way to increase the level of classification accuracy. In this study, a new method was used, by using a training set in the form of words and phrases instead of just using a training set in the form of words only. With this method, the level of classification accuracy of the two languages is increased.

scholarly journals Distributed Power Grid Fault Diagnosis Based on Naive Bayesian Network and D-S Evidence Theory

2020 ◽  
Vol 1549 ◽  
pp. 052077
Author(s):  
Xiaoqin Liu ◽  
Xing Yang ◽  
Chengyu Li ◽  
Jinsong Liu ◽  
Fengwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Power Grid ◽  
Evidence Theory ◽  
Distributed Power ◽  
Naive Bayesian ◽  
Naïve Bayesian
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