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

• Main Authors: Razali, A., Daud, S. M., Zin, N. A. M., Shahidi, F.
• Format: Article
• Language: English
• Published: Science and Information Organization 2020
• Subjects: QA75 Electronic computers. Computer science
• 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
• Institution: Universiti Teknologi Malaysia

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...