Mousa Al-Akhras1,2,*, Zainab Darwish2, Samer Atawneh1, Mohamed Habib1,3
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3719-3749, 2022, DOI:10.32604/cmc.2022.025196
Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with different instance reduction techniques, namely:… More >
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