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• W2894239351 abstract "There are various ways of web page classification but they take higher time to compute with lesser accuracy. Hence, there is a need to invent an efficient algorithm in order to reduce time and increase web page classification result. It is generally find that a few tags like title can contain the principle substance of text, and these patterns may have an impact on the adequacy of text classification. Although, the most widely recognized text weighting calculations, called term frequency inverse documents frequency (TF-IDF) doesn’t consider the structure of website pages. To take care of this issue, another feature tags weighting calculation is put in advanced. It thinks about the web page structure data like title, Meta tags, head etc. also content the useful information. In this proposed study first web site pages data are pre-processed and find text weight using TFIDF, after that using feature tag weighting calculation, frequent and important tags will find; then on the basis of text weight and tags weight, web document will classify." @default.
• W2894239351 created "2018-10-05" @default.
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• W2894239351 date "2018-01-01" @default.
• W2894239351 modified "2023-10-02" @default.
• W2894239351 title "Exploring Structure Oriented Feature Tag Weighting Algorithm for Web Documents Identification" @default.
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• W2894239351 doi "https://doi.org/10.1007/978-981-13-1936-5_20" @default.
• W2894239351 hasPublicationYear "2018" @default.
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