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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.<\/jats:p>","DOI":"10.1515\/jisys-2024-0400","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T14:35:25Z","timestamp":1740666925000},"source":"Crossref","is-referenced-by-count":0,"title":["Hybrid modeling of structure extension and instance weighting for naive Bayes"],"prefix":"10.1515","volume":"34","author":[{"given":"Liangjun","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer, Hubei University of Education , Wuhan , Hubei, 430205 , China"},{"name":"Hubei Co-Innovation Center of Basic Education Information Technology Services, Hubei University of Education , Wuhan , Hubei, 430205 , China"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer, Hubei University of Education , Wuhan , Hubei, 430205 , China"},{"name":"School of Computer and Information Engineering, Hubei Normal University , Huangshi , Hubei, 435002 , China"}]},{"given":"Xian","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer, Hubei University of Education , Wuhan , Hubei, 430205 , China"},{"name":"School of Computer and Information Engineering, Hubei Normal University , Huangshi , Hubei, 435002 , China"}]},{"given":"Xiaomin","family":"Wu","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd. , Wuhan , Hubei, 430077 , China"}]}],"member":"374","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"2025122009032276930_j_jisys-2024-0400_ref_001","doi-asserted-by":"crossref","unstructured":"Jiang L, Li C, Wang S, Zhang L. 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Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. In: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics; 1999. p. 225\u201330."},{"key":"2025122009032276930_j_jisys-2024-0400_ref_034","doi-asserted-by":"crossref","unstructured":"Qiu C, Jiang L, Li C. Not always simple classification: Learning SuperParent for class probability estimation. Expert Syst Appl. 2015;42(13):5433\u201340. 10.1016\/J.ESWA.2015.02.049.","DOI":"10.1016\/j.eswa.2015.02.049"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_035","doi-asserted-by":"crossref","unstructured":"Jiang L, Zhang H, Cai Z. A novel Bayes model: Hidden naive Bayes. IEEE Trans Knowledge Data Eng. 2009;21(10):1361\u201371. 10.1109\/TKDE.2008.234.","DOI":"10.1109\/TKDE.2008.234"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_036","doi-asserted-by":"crossref","unstructured":"Zhang H, Jiang L, Yu L. Class-specific attribute value weighting for Naive Bayes. Inform Sci. 2020;508:260\u201374. 10.1016\/J.INS.2019.08.071.","DOI":"10.1016\/j.ins.2019.08.071"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_037","unstructured":"Frank E, Hall M, Pfahringer B. Locally weighted naive bayes. In: Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc.; 2002. p. 249\u201356. 10.48550\/arXiv.1212.2487."},{"key":"2025122009032276930_j_jisys-2024-0400_ref_038","doi-asserted-by":"crossref","unstructured":"Jiang L, Wang D, Zhang H, Cai Z, Huang B. Using Instance cloning to Improve Naive Bayes for Ranking. Int J Pattern Recognit Artif Intell. 2008;22(6):1121\u201340. 10.1142\/S0218001408006703.","DOI":"10.1142\/S0218001408006703"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_039","doi-asserted-by":"crossref","unstructured":"Blanquero R, Carrizosa E, Ram\u00edrez-Cobo P, Sillero-Denamiel MR. Variable selection for Na\u00efve Bayes classification. Comput Operat Res. 2021;135:105456. 10.1016\/J.COR.2021.105456.","DOI":"10.1016\/j.cor.2021.105456"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_040","doi-asserted-by":"crossref","unstructured":"Jiang L, Zhang L, Li C, Wu J. A correlation-based feature weighting filter for naive Bayes. IEEE Trans Knowledge Data Eng. 2019;31(2):201\u201313. 10.1109\/TKDE.2018.2836440.","DOI":"10.1109\/TKDE.2018.2836440"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_041","unstructured":"Kelly M, Longjohn R, Nottingham K. The UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu."},{"key":"2025122009032276930_j_jisys-2024-0400_ref_042","doi-asserted-by":"crossref","unstructured":"Witten IH, Frank E, Hall MA. Data mining: practical machine learning tools and techniques, 3rd Edition. 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J Mult Valued Log Soft Comput. 2015;17:255\u201387. 10.1016\/j.jlap.2009.12.002."},{"key":"2025122009032276930_j_jisys-2024-0400_ref_046","doi-asserted-by":"crossref","unstructured":"Windi WA, Taufiq M, Muhammad T. Implementasi Wilcoxon Signed Rank Test Untuk Mengukur Efektifitas Pemberian Video Tutorial Dan Ppt Untuk Mengukur Nilai Teori. Produktif: Jurnal Ilmiah Pendidikan Teknologi Informasi. 2021;5(1):405\u201310.","DOI":"10.35568\/produktif.v5i1.1004"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_047","doi-asserted-by":"crossref","unstructured":"Obulesu O, Kallam S, Dhiman G, Patan R, Kadiyala R, Raparthi Y, et al. Adaptive diagnosis of lung cancer by deep learning classification using Wilcoxon gain and generator. J Healthcare Eng. 2021;2021:5912051. 10.1155\/2021\/5912051.","DOI":"10.1155\/2021\/5912051"},{"key":"2025122009032276930_j_jisys-2024-0400_ref_048","doi-asserted-by":"crossref","unstructured":"Hodson TO. 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