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Evidence and scenario sensitivities in naive Bayesian classifiers

Silja Renooij, Linda C. van der Gaag
2008 International Journal of Approximate Reasoning  
Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated network classifiers, even in view of inaccuracies in their parameters.  ...  In this paper, we study the effects of such parameter inaccuracies by investigating the sensitivity functions of a naive Bayesian classifier.  ...  Although it is applicable to Bayesian networks in general, in this section we discuss scenario sensitivity in the context of naive Bayesian classifiers.  ... 
doi:10.1016/j.ijar.2008.02.008 fatcat:pd5vamaocfe6zdtcwbvtmrxyvy

Bayesian Methods for Multivariate Modeling of Pleiotropic SNP Associations and Genetic Risk Prediction

Stephen W. Hartley, Stefano Monti, Ching-Ti Liu, Martin H. Steinberg, Paola Sebastiani
2012 Frontiers in Genetics  
In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal.  ...  In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction.  ...  Gordeuk, Gregory Kato, Caterina Minniti, James Taylor, Andrew Campbell, and Lori Luchtman-Jones, NCT00495638, with the Pulmonary Hypertension and the Hypoxic Response in SCD study (PUSH), Carolyn Hoppe  ... 
doi:10.3389/fgene.2012.00176 pmid:22973300 pmcid:PMC3438684 fatcat:56i5l52yqvhjrjyy4txe7qkanq

Classification of Imbalanced Malaria Disease Using Naïve Bayesian Algorithm

T Sajana, M R.Narasingarao
2018 International Journal of Engineering & Technology  
The aim of this research is to propose a comparative study on classifying the imbalanced malaria disease data using Naive Bayesian classifier in different environments like weka and using an R-language  ...  Synthetic Minority Oversampling Technique (SMOTE) technique has been used to balance the class distribution and then we performed a comparative study on the dataset using Naïve Bayesian algorithm in various  ...  Applied SMOTE algorithm to balance the class distribution and then conducted a comparison study of Naive Bayesian classifier in both Weka and R programming environments.  ... 
doi:10.14419/ijet.v7i2.7.10978 fatcat:7hc4dtdfyzhkbpb6oyildceige

A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks

Farnoush Falahatraftar, Samuel Pierre, Steven Chamberland
2021 IEEE Access  
Numerical results show that the proposed Naive Bayesian classifier is more reliable and stable and can accurately predict the data flow warning state in HetVNET.  ...  In addition, we propose a centralized and dynamic cloud-fog-based architecture for HetVNET. The Naive Bayesian network congestion warning classification method can be applied in this architecture.  ...  a Naive Bayesian classifier.  ... 
doi:10.1109/access.2021.3108425 fatcat:s4bhss2k5nbxpgri7venr6agji

Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [article]

Eduardo Avila, Marcio Dorn, Clarice Sampaio Alho, Alessandro Kahmann
2020 arXiv   pre-print
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.  ...  Methods: A Naive-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests.  ...  Machine Learning Analysis -Naïve Bayes Classifier Machine learning (ML) is a field of study in computer science and statistics dedicated to the execution of computational tasks through algorithms that  ... 
arXiv:2005.10227v1 fatcat:twam5ov655c5zpmtnleydlixda

Improved Naive Bayes Classification for Joint Investment Plan

Mufda Jameel Alrawashdeh
2022 WSEAS Transactions on Mathematics  
Hence, this paper uses a method to improve the accuracy of Naïve Bayes approach by using a learning structure of feature variables in the model and apply it to joint investment plan applications.  ...  The introduction and use of new applied problem is not only helpful to show the application of the field but also attract researchers from social science to apply and use Bayes based methods which in turn  ...  It further attempts and show that a learning in between independent nodes as assumed by Naïve Bayes classifier approach produce a better result.  ... 
doi:10.37394/23206.2022.21.6 fatcat:qc5ejosu4jenpni2o5fy2y3mnm

Hospital profiling using Bayesian decision theory [article]

Johannes Hengelbrock, Johannes Rauh, Jona Cederbaum, Maximilian Kaehler, Michael Hoehle
2021 medRxiv   pre-print
We propose paradigmatic utility functions for the two pathways external reporting and change in care delivery and compare the resulting optimal decision rules with regard to their threshold values, sensitivity  ...  for external reporting, with consequences for both sensitivity and specificity.  ...  has a high sensitivity for high-volume providers, which can be seen in the out-of-control-scenario.  ... 
doi:10.1101/2021.06.23.21259367 fatcat:uoqsjq6oa5aotcggvj46hf6dvi

Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [article]

Marcio Dorn, Eduardo Avila, Clarice Sampaio Alho, Alessandro Kahmann
2020 medRxiv   pre-print
Methods: A Naïve-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests.  ...  Results: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.  ...  Machine Learning Analysis -Naïve Bayes Classifier Machine learning (ML) is a field of study in computer science and statistics dedicated to the execution of computational tasks through algorithms that  ... 
doi:10.1101/2020.05.09.20096818 fatcat:jug3mfbijbgl7kocm2kd4fs4bu

An Efficient Detection of HCC-recurrence in Clinical Data Processing using Boosted Decision Tree Classifier

P. Radha, R. Divya
2020 Procedia Computer Science  
A multiple(many) measurement of naïve Bayesian (MMNB) was also used as an additional classification model for performance evaluation.  ...  There are totally, 475 clinical datasets collected, in which 198 hepatocellular carcinoma (HCC) and 277 non hepatocellular carcinoma (non-HCC) were utilized in this investigation study.  ...  The Naive Bayesian classifier meet the expense of fast, highly accessible and it can work with lesser amounts of preparation data, and can also accommodate an enormous number of preparation samples which  ... 
doi:10.1016/j.procs.2020.03.196 fatcat:op6dypwsx5dp3nc5rtiyjcaosu

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance

Kirsten Vallmuur, Helen R Marucci-Wellman, Jennifer A Taylor, Mark Lehto, Helen L Corns, Gordon S Smith
2016 Injury Prevention  
The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large  ...  Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives  ...  fuzzy and naïve Bayesian models with single word predictors.  ... 
doi:10.1136/injuryprev-2015-041813 pmid:26728004 pmcid:PMC4852152 fatcat:o26wyjrvcjfmdfzxnsswynbuaq

Improving diagnostic recognition of primary hyperparathyroidism with machine learning

Yash R. Somnay, Mark Craven, Kelly L. McCoy, Sally E. Carty, Tracy S. Wang, Caprice C. Greenberg, David F. Schneider
2017 Surgery  
However, in mild disease cases, the Bayesian network model correctly classified 71.1% of patients with normal calcium and 92.1% with normal PTH levels preoperatively.  ...  Measures of accuracy included area under the ROC curve, precision (sensitivity), and positive and negative predictive value.  ...  Acknowledgments This work was supported by NIH UL1TR000427 and NIH KL2TR000428.  ... 
doi:10.1016/j.surg.2016.09.044 pmid:27989606 pmcid:PMC5367958 fatcat:ntjfc2jqgndnrg6bcrmnoz47qu

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Fareeha Sameen, Abdul Momin Kazi, Majida Kazmi, Munir A Abbasi, Saad Ahmed Qazi, Lampros K Stergioulas
2022 Computers Materials & Continua  
This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.  ...  The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity.  ...  Bayesian TAN achieved an accuracy of 90.91% and Naïve Bayes attained an accuracy of 86.36%. Chandir et al.  ... 
doi:10.32604/cmc.2022.019167 fatcat:5tlglflvqrakvbfppcehd64isa

Generation, testing and recommendation of teaching materials using classification learning

Arnoldo Rodriguez
2008 International Journal of Advanced Media and Communication  
AI principles and techniques are applied to the generation, testing and recommendation of teaching materials (computer network topologies), as a way to providing Web-based help.  ...  The subject of application of this study is teaching decision-making skills in network design.  ...  ), and the Ministerio de Ciencia y Tecnología de Costa Rica (The Ministry of Science and Technology of Costa Rica).  ... 
doi:10.1504/ijamc.2008.020182 fatcat:4gz55licojdejl33dpg7lv2vym

A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data

Wenqi Wu, James Stamey, David Kahle
2015 International Journal of Environmental Research and Public Health  
It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis.  ...  Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion.  ...  Acknowledgments The authors would like to thank four anonymous reviewers and the Editors for substantive comments in the preparation of this article.  ... 
doi:10.3390/ijerph120910648 pmid:26343704 pmcid:PMC4586634 fatcat:argy2wqkwjdqdkyw5dm5moczqi

Challenges of Testing Machine Learning Applications

Song Huang
2018 International Journal of Performability Engineering  
Machine learning applications have achieved impressive results in many areas and provided effective solution to deal with image recognition, automatic driven, voice processing etc. problems.  ...  As these applications are adopted by multiple critical areas, their reliability and robustness becomes more and more important.  ...  The ML algorithms are data sensitive; the rule and logic (i.e. the probability in Naive Bayesian classifier and weights in DNN classifier) are learned from the training data.  ... 
doi:10.23940/ijpe.18.06.p18.12751282 fatcat:d7lyhfzxzzhpbolm2rogdn6api
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