Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/9/9/99 |
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doaj-dd5d7d4349cd4ac6ad3f94b4d55d93982021-09-25T23:56:43ZengMDPI AGComputation2079-31972021-09-019999910.3390/computation9090099Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical DatasetsPannapa Changpetch0Apasiri Pitpeng1Sasiprapa Hiriote2Chumpol Yuangyai3Department of Mathematics, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Mathematics, Faculty of Science, Mahidol University, Bangkok 10400, ThailandDepartment of Statistics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, ThailandDepartment of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandIn this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.https://www.mdpi.com/2079-3197/9/9/99association rules analysisclassification treediscretizationinteractionsnaïve Bayes |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Pannapa Changpetch Apasiri Pitpeng Sasiprapa Hiriote Chumpol Yuangyai |
| spellingShingle |
Pannapa Changpetch Apasiri Pitpeng Sasiprapa Hiriote Chumpol Yuangyai Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets Computation association rules analysis classification tree discretization interactions naïve Bayes |
| author_facet |
Pannapa Changpetch Apasiri Pitpeng Sasiprapa Hiriote Chumpol Yuangyai |
| author_sort |
Pannapa Changpetch |
| title |
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets |
| title_short |
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets |
| title_full |
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets |
| title_fullStr |
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets |
| title_full_unstemmed |
Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets |
| title_sort |
integrating data mining techniques for naïve bayes classification: applications to medical datasets |
| publisher |
MDPI AG |
| series |
Computation |
| issn |
2079-3197 |
| publishDate |
2021-09-01 |
| description |
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve Bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas. |
| topic |
association rules analysis classification tree discretization interactions naïve Bayes |
| url |
https://www.mdpi.com/2079-3197/9/9/99 |
| work_keys_str_mv |
AT pannapachangpetch integratingdataminingtechniquesfornaivebayesclassificationapplicationstomedicaldatasets AT apasiripitpeng integratingdataminingtechniquesfornaivebayesclassificationapplicationstomedicaldatasets AT sasiprapahiriote integratingdataminingtechniquesfornaivebayesclassificationapplicationstomedicaldatasets AT chumpolyuangyai integratingdataminingtechniquesfornaivebayesclassificationapplicationstomedicaldatasets |
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1717367529949626368 |