Vol. 5 No. 2 (2021)
Articles

Classification of Web Page by Using Neural Networks

Published 2021-07-26

Abstract

The exclusive growth in the WWW World Wide Web page window makes the internet growing very fast. Therefore, classifiers of the web pages become more challenging. The proposed system is about using Neural Networks (N.N.) to classify web documents.  In this work, New Web Page Classification Method (WPCM) is proposed. The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). The fixed number of regular words from each class will be used as a feature vectors with the reduced features from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM provides high quality classification accuracy with the sports news datasets. These features reflect the motivation of this work and it is a new approach to classify the web documents through using the Neural Networks. The applications for this work will be widely requested from media, sports, newspapers, journals, etc. because the proposed system offers fast method for classification due to the summarizing of time by reducing the dimension and features of the word, also removing stop words will save spaces for storing document contents and reduce time taken during the search process