IIUC Journal of Science and Engineering

Permanent URI for this communityhttp://dspace.iiuc.ac.bd/handle/123456789/8468

Browse

Filters

2023 - 20242

Settings

Subject: search.filters.subject.Imbalanced dataset

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Covid-19 detection using dominant SMOTE in imbalance classification
    (Center for Research and Publication (CRP), 2024-12) Islam, Saiful
    Global healthcare systems have faced difficulties since the start of the COVID-19 epidemic. For overburdened hospitals, identifying positive patients is a simple and effective fix. The disproportionate distribution of classes poses a significant challenge in identifying the positive case of COVID-19, leading to biased prediction outcomes favoring dominant classes. Consequently, classifiers struggle to learn from imbalanced datasets, resulting in reduced performance. Various techniques, such as oversampling, undersampling, and hybrid sampling, have been proposed to mitigate this issue. However, the Synthetic Minority Oversampling Technique (SMOTE) remains a commonly utilized resampling method despite its limitations, including class mixture. To address these shortcomings, I introduce Dominant SMOTE, a modified version of SMOTE. The proposed method comprises of developing a dominant sample selection approach based on numerical attribute values from the minority class, and selecting the nearest neighbors from the majority class for each minority class sample based on dominance values to achieve balanced dataset. The proposed method is compared with traditional SMOTE and Out-Layer SMOTE, evaluating accuracy, precision, recall, and F1-score on two benchmark datasets. The results indicate that the proposed model outperforms than both the traditional SMOTE and Out-Layer SMOTE.
  • No Thumbnail Available
    Item
    Unleashing class imbalance problem in loan dataset through a novel oversampling approach based on FCM
    (CRP, International Islamic University Chittagong, 2023-12) Akter, Subrina
    Commercial companies highly depend on loan approval models trained by machine learning and statistical methods to predict loan status. However, imbalanced datasets present a key challenge in this sector. Addressing this issue, this paper proposes a new oversampling method based on Fuzzy C means clustering. This clustering algorithm assigns the instances to several groups by assigning a flexible degree of membership. K-Nearest Neighbor and the Decision Tree served as the basic classifiers in an extensive test on the Kaggle loan dataset. Three distinct imbalanced ratios—2.2, 4.2, and 8.45—were used in the experiment. The effectiveness of the recommended strategy was compared with SMOTE and WBOT using 5-fold CV. The outcomes showed that the proposed method outperformed both SMOTE and WBOT, obtaining higher average F-measure and G-mean values across the machine learning algorithms. These findings show how the suggested method may correct class imbalance while also enhancing prediction accuracy in the context of loan acceptance.