Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification...

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Vydané v:Informatica (Ljubljana) Ročník 46; číslo 3; s. 373 - 381
Hlavný autor: Wang, Rong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 01.09.2022
Predmet:
Algorithms
Automatic classification
Bans
Bayesian analysis
Classification
Decision trees
Documents
Internet
Machine learning
Modular design
Modular systems
Modules
Ontology
Semantics
Subject specialists
Vector spaces
World Wide Web
ISSN:0350-5596, 1854-3871
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Shrnutí:World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0350-5596
1854-3871
DOI:10.31449/inf.v46i3.3970