Year: 2024
Type: conference-paper
Abstract: Imbalanced data is common and presents significant challenge towards classification of data.In this research, we present a combination of two techniques used for handling class imbalance in datasets, SMOTE (Synthetic Minority Over-sampling Technique) and Tomek Links.Each strategy handles the class imbalance problem in a unique way, and their combination attempts to create a more balanced and cleaner dataset for training machine learning models to handle binary classification by addressing problematic or difficult-to-classify data.Machine learning classifiers used in this study are K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Logistic Regression, Decision Tree (DT), Random Forest (RF), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBM), AdaBoost and Catboost.It has been discovered that the mean F1 score for resampled datasets provides more trustworthy results for forecasting floods.
DOI: 10.5109/7323359
Institutions: University of Technology Malaysia, State University of Malang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404653525