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DOI: 10.14569/IJACSA.2020.0110171
PDF

Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison

Author 1: Ansari Razali
Author 2: Salwani Mohd Daud
Author 3: Nor Azan Mat Zin
Author 4: Faezehsadat Shahidi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.

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Abstract: The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naïve Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in term of accuracy and processing time. This research uses BBC dataset that has five predefined categories. The result demonstrates that classifiers' performance is better without word stemming, whereby all classifiers show higher classification accuracy, with the highest accuracy produced by NB and SVM at 97% for F1 score, while NB takes shorter training time than SVM. With word stemming, the effect on training and classification time is negligible, except on Multilayer Perceptron in which word stemming has effectively reduced the training time.

Keywords: Web page classification; stemming; machine learning; Naïve Bayes; k-NN; SVM; multilayer perceptron

Ansari Razali, Salwani Mohd Daud, Nor Azan Mat Zin and Faezehsadat Shahidi, “Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110171

@article{Razali2020,
title = {Stemming Text-based Web Page Classification using Machine Learning Algorithms: A Comparison},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110171},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110171},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Ansari Razali and Salwani Mohd Daud and Nor Azan Mat Zin and Faezehsadat Shahidi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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