Machine Learning Algorithms in Web Page Classification
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The contemporary world is heavily influenced by web technology, with a significant increase in web information every year. Manual classification of web page documents proves to be both time-consuming and inaccurate, given the abundance of irrelevant, redundant, and noisy information present in web pages. Therefore, an automatic web page classification system becomes essential. Web page classification plays a crucial role in information management and retrieval tasks. Feature selection is a pivotal step in achieving accurate web page classification.Web pages typically contain a large number of ...
The contemporary world is heavily influenced by web technology, with a significant increase in web information every year. Manual classification of web page documents proves to be both time-consuming and inaccurate, given the abundance of irrelevant, redundant, and noisy information present in web pages. Therefore, an automatic web page classification system becomes essential. Web page classification plays a crucial role in information management and retrieval tasks. Feature selection is a pivotal step in achieving accurate web page classification.Web pages typically contain a large number of features, which can adversely affect classification accuracy. The primary objective of the proposed research is to develop a hybrid feature selection approach that is not only efficient but also effective in automatically classifying web pages. This approach not only enhances classification accuracy but also aids web search tools in delivering relevant results within the appropriate category.
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