Stemming text-based web page classification using machine learning algorithms: a comparison

The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive Bayes (NB), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Multilayer Perceptron (MP). Each classifiers' performance is evaluated in...

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Main Authors: Razali, A., Daud, S. M., Zin, N. A. M., Shahidi, F.
Format: Article
Language:English
Published: Science and Information Organization 2020
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Online Access:http://eprints.utm.my/id/eprint/86791/1/AnsariRazali2020_StemmingTextBasedWebPageClassification.pdf
http://eprints.utm.my/id/eprint/86791/
https://dx.doi.org/10.14569/ijacsa.2020.0110171
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.867912020-09-30T09:08:19Z http://eprints.utm.my/id/eprint/86791/ Stemming text-based web page classification using machine learning algorithms: a comparison Razali, A. Daud, S. M. Zin, N. A. M. Shahidi, F. QA75 Electronic computers. Computer science The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive 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. Science and Information Organization 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86791/1/AnsariRazali2020_StemmingTextBasedWebPageClassification.pdf Razali, A. and Daud, S. M. and Zin, N. A. M. and Shahidi, F. (2020) Stemming text-based web page classification using machine learning algorithms: a comparison. International Journal of Advanced Computer Science and Applications, 11 (1). pp. 570-576. ISSN 2158-107X https://dx.doi.org/10.14569/ijacsa.2020.0110171 DOI:10.14569/ijacsa.2020.0110171
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Razali, A.
Daud, S. M.
Zin, N. A. M.
Shahidi, F.
Stemming text-based web page classification using machine learning algorithms: a comparison
description The research aim is to determine the effect of word-stemming in web pages classification using different machine learning classifiers, namely Naive 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.
format Article
author Razali, A.
Daud, S. M.
Zin, N. A. M.
Shahidi, F.
author_facet Razali, A.
Daud, S. M.
Zin, N. A. M.
Shahidi, F.
author_sort Razali, A.
title Stemming text-based web page classification using machine learning algorithms: a comparison
title_short Stemming text-based web page classification using machine learning algorithms: a comparison
title_full Stemming text-based web page classification using machine learning algorithms: a comparison
title_fullStr Stemming text-based web page classification using machine learning algorithms: a comparison
title_full_unstemmed Stemming text-based web page classification using machine learning algorithms: a comparison
title_sort stemming text-based web page classification using machine learning algorithms: a comparison
publisher Science and Information Organization
publishDate 2020
url http://eprints.utm.my/id/eprint/86791/1/AnsariRazali2020_StemmingTextBasedWebPageClassification.pdf
http://eprints.utm.my/id/eprint/86791/
https://dx.doi.org/10.14569/ijacsa.2020.0110171
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