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.
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Acknowledgements
I would like to thank all the people those who helped me to give the knowledge about these research papers. I am thankful to Dr. Prateek Srivastava & Dr. Prasun Chakrabarti to encourage and guided in this topic which helped me to speed up the work for structure based web page classification for fast search. Finally, I like to acknowledge all the websites and IEEE papers which I have gone through and referred to create this research paper.
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Verma, K., Srivastava, P., Chakrabarti, P. (2018). Exploring Structure Oriented Feature Tag Weighting Algorithm for Web Documents Identification. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_20
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DOI: https://doi.org/10.1007/978-981-13-1936-5_20
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