Classification of Web Pages Based on Search Engine Optimization Quality using Random Forest Algorithm

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Shariatipour Mohammad Hosein; Rahmanimanesh Mohammad; Parseh Mohammad Javad

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Classification of Web Pages Based on Search Engine Optimization Quality using Random Forest Algorithm

Author(s)

Shariatipour Mohammad Hosein | Rahmanimanesh Mohammad | Parseh Mohammad Javad

Keywords

Search Engine Optimization

Machine Learning

Ensemble Learning

Web Page Classification

Random Forest Algorithm

Abstract

In the evening where ranking on the search engine result pages is directly related to increasing the number of visitors and the progress and development of a business, Search Engine Optimization or SEO is a process that helps to gain a higher ranking. Websites can be classified with the help of Machine Learning techniques based on the quality of setting SEO guidelines. Classification algorithms are combined with each other with the aim of increasing classification accuracy and are used as an ensemble classification model. In this article, we implement an ensemble classification model with the help of a Random Forest Algorithm, which places web pages in one of the predefined classes based on SEO quality. The obtained results show that the accuracy of the constructed model is between 70. 50% and 73. 17% and is more accurate than previous works in which ensemble classification algorithms were not used. The built model can help developers build automatic software for detecting the SEO quality of web pages.

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APA: Copy

Shariatipour, Mohammad Hosein, Rahmanimanesh, Mohammad, & Parseh, Mohammad Javad. (). . . SID. https://sid.ir/paper/1046856/en

Vancouver: Copy

Shariatipour Mohammad Hosein, Rahmanimanesh Mohammad, Parseh Mohammad Javad. . . Available from: https://sid.ir/paper/1046856/en

IEEE: Copy

Mohammad Hosein Shariatipour, Mohammad Rahmanimanesh, and Mohammad Javad Parseh, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046856/en

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