Hybrid modeling of structure extension and instance weighting for naive Bayes
Due to robustness and efficiency, naive Bayes (NB) remains among the top ten data mining algorithms. However, the required conditional independence assumption more or less limits its classification performance. Of numerous approaches to improving NB, structure extension and instance weighting have b...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-02-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2024-0400 |
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| _version_ | 1850240156049604608 |
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| author | Yu Liangjun Wang Di Zhou Xian Wu Xiaomin |
| author_facet | Yu Liangjun Wang Di Zhou Xian Wu Xiaomin |
| author_sort | Yu Liangjun |
| collection | DOAJ |
| description | Due to robustness and efficiency, naive Bayes (NB) remains among the top ten data mining algorithms. However, the required conditional independence assumption more or less limits its classification performance. Of numerous approaches to improving NB, structure extension and instance weighting have both achieved remarkable improvements. To make full use of their complementary and consensus advantages, this article proposes a hybrid modeling approach to combining structure extension with instance weighting. We call the resulting model instance weighted averaged one-dependence estimators (IWAODE). In IWAODE, the dependencies among attributes are modeled by an ensemble of one-dependence estimators, and the corresponding probabilities are estimated from attribute value frequency-weighted training instances. The classification performance of IWAODE is experimentally validated on a large number of datasets. |
| format | Article |
| id | doaj-art-c1afd63cb8f54f588b58d29993ca3a94 |
| institution | OA Journals |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-c1afd63cb8f54f588b58d29993ca3a942025-08-20T02:00:55ZengDe GruyterJournal of Intelligent Systems2191-026X2025-02-01341263910.1515/jisys-2024-0400Hybrid modeling of structure extension and instance weighting for naive BayesYu Liangjun0Wang Di1Zhou Xian2Wu Xiaomin3College of Computer, Hubei University of Education, Wuhan, Hubei, 430205, ChinaCollege of Computer, Hubei University of Education, Wuhan, Hubei, 430205, ChinaCollege of Computer, Hubei University of Education, Wuhan, Hubei, 430205, ChinaElectric Power Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan, Hubei, 430077, ChinaDue to robustness and efficiency, naive Bayes (NB) remains among the top ten data mining algorithms. However, the required conditional independence assumption more or less limits its classification performance. Of numerous approaches to improving NB, structure extension and instance weighting have both achieved remarkable improvements. To make full use of their complementary and consensus advantages, this article proposes a hybrid modeling approach to combining structure extension with instance weighting. We call the resulting model instance weighted averaged one-dependence estimators (IWAODE). In IWAODE, the dependencies among attributes are modeled by an ensemble of one-dependence estimators, and the corresponding probabilities are estimated from attribute value frequency-weighted training instances. The classification performance of IWAODE is experimentally validated on a large number of datasets.https://doi.org/10.1515/jisys-2024-0400naive bayesstructure extensioninstance weightinghybrid modeling |
| spellingShingle | Yu Liangjun Wang Di Zhou Xian Wu Xiaomin Hybrid modeling of structure extension and instance weighting for naive Bayes Journal of Intelligent Systems naive bayes structure extension instance weighting hybrid modeling |
| title | Hybrid modeling of structure extension and instance weighting for naive Bayes |
| title_full | Hybrid modeling of structure extension and instance weighting for naive Bayes |
| title_fullStr | Hybrid modeling of structure extension and instance weighting for naive Bayes |
| title_full_unstemmed | Hybrid modeling of structure extension and instance weighting for naive Bayes |
| title_short | Hybrid modeling of structure extension and instance weighting for naive Bayes |
| title_sort | hybrid modeling of structure extension and instance weighting for naive bayes |
| topic | naive bayes structure extension instance weighting hybrid modeling |
| url | https://doi.org/10.1515/jisys-2024-0400 |
| work_keys_str_mv | AT yuliangjun hybridmodelingofstructureextensionandinstanceweightingfornaivebayes AT wangdi hybridmodelingofstructureextensionandinstanceweightingfornaivebayes AT zhouxian hybridmodelingofstructureextensionandinstanceweightingfornaivebayes AT wuxiaomin hybridmodelingofstructureextensionandinstanceweightingfornaivebayes |