| dc.description.abstract | In the field of data mining, data collection often comes with various data quality issues, including duplicate values, missing values, outliers, and data inconsistency, which indirectly affect the difficulty of extracting useful information. Furthermore, the class imbalance has become an important issue in data mining due to the different probabilities of events in the real world. This problem leads to decreased predictive performance for minority classes in model prediction and classification, negatively impacting the accuracy and reliability of data analysis.
Therefore, this paper focuses on addressing the class imbalance problem. Based on previous literature, this study employs data-level approaches and flexibly combines different
classification algorithms to resample class-imbalanced datasets. It explores whether adjusting the class proportions under different resampling techniques affects the classification performance. Moreover, since existing literature does not propose the integration of individual classifiers trained with different resampling techniques to build multiple classifiers or merging different resampled samples with single classifiers or ensemble classifiers, this research proposes homogeneous and heterogeneous methods based on ensemble methods to explore which combination approach can better handle class imbalance problems under different processing flows.
Through experimental results, this study demonstrates that resampling class-imbalanced datasets using data-level techniques in data preprocessing can effectively improve classification performance, and the balance ratio of resampled minority and majority classes significantly influences classifier performance. In the comprehensive comparison between homogeneous and heterogeneous methods, there is no statistical difference between multiple classifiers and the single classifier or ensemble classifier using sample merging. However, heterogeneous methods, compared to homogeneous methods, are more capable of exploring the best combinations with different classification algorithms to enhance classification accuracy (AUC). These experimental results provide directions for further expansion and improvement of ensemble classifiers and offer more choices and optimization strategies for addressing class imbalance problems. | en_US |