Weighted Naive Bayes Model for Semi-Structured Document Categorization

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dc.creatorMarteau, Pierre-François
dc.creatorMénier, Gilbas
dc.creatorPopovici, Eugen
dc.date2009-01-04
dc.date.accessioned2026-07-07T12:24:31Z
dc.date.available2026-07-07T12:24:31Z
dc.descriptionThe aim of this paper is the supervised classification of semi-structured data. A formal model based on bayesian classification is developed while addressing the integration of the document structure into classification tasks. We define what we call the structural context of occurrence for unstructured data, and we derive a recursive formulation in which parameters are used to weight the contribution of structural element relatively to the others. A simplified version of this formal model is implemented to carry out textual documents classification experiments. First results show, for a adhoc weighting strategy, that the structural context of word occurrences has a significant impact on classification results comparing to the performance of a simple multinomial naive Bayes classifier. The proposed implementation competes on the Reuters-21578 data with the SVM classifier associated or not with the splitting of structural components. These results encourage exploring the learning of acceptable weighting strategies for this model, in particular boosting strategies.
dc.identifierhttps://arxiv.org/abs/0901.0358
dc.identifierhttp://arxiv.org/abs/0901.0358
dc.identifier1st International Conference on Multidisciplinary Information Sciences and Technologies InSciT2006, Merida : Espagne (2006)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/214347
dc.subjectInformation Retrieval
dc.subjectH.3.3
dc.titleWeighted Naive Bayes Model for Semi-Structured Document Categorization
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