A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning

2015 ◽  
Vol 32 (3) ◽  
pp. 458-479
Author(s):  
Sunil Aryal ◽  
Kai Ming Ting
Keyword(s):  
Bayesian Classifier ◽  
Ensemble Approach ◽  
Classifier Learning

scholarly journals A Bayesian Classifier Learning Algorithm Based on Optimization Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sanyang Liu ◽  
Mingmin Zhu ◽  
Youlong Yang
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Bayesian Classifier ◽  
Bayes Classifier ◽  
Independence Assumption ◽  
Maximum Value ◽  
Chi Squared ◽  
Classifier Learning

Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes and affects its classification performance. In this paper, we summarize the existing improved algorithms and propose a Bayesian classifier learning algorithm based on optimization model (BC-OM). BC-OM uses the chi-squared statistic to estimate the dependence coefficients among attributes, with which it constructs the objective function as an overall measure of the dependence for a classifier structure. Therefore, a problem of searching for an optimal classifier can be turned into finding the maximum value of the objective function in feasible fields. In addition, we have proved the existence and uniqueness of the numerical solution. BC-OM offers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Influence of feature weight on text categorization performance of Bayesian classifier

2009 ◽  
Vol 28 (12) ◽  
pp. 3080-3083 ◽  
Author(s):  
Xiu-mei GAO ◽  
Fang CHEN ◽  
Feng-xi SONG ◽  
Zhong JIN
Keyword(s):  
Text Categorization ◽  
Bayesian Classifier ◽  
Feature Weight

scholarly journals Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172859-172868
Author(s):  
Zhengwei Ma ◽  
Sensen Guo ◽  
Gang Xu ◽  
Saddam Aziz
Keyword(s):  
Wind Speed ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Ensemble Approach ◽  
Meta Learning
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