Wen Yee Wong1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045
Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There
are significantly more samples for day-to-day classes, while rare events such
as polluted classes are uncommon. Consequently, the limited availability of
minority outcomes lowers the classifier’s overall reliability. This study assesses
the capability of machine learning (ML) algorithms in tackling imbalanced
water quality data based on the metrics of precision, recall, and F1 score. It
intends to balance the misled accuracy towards the majority of data. Hence, 10
ML algorithms of its performance are compared. The classifiers included are
AdaBoost, Support Vector… More >
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