Conference Papers

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    Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem
    (Elsevier B.V., 2024) Sneha, H.R.; Annappa, B.
    Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency. © 2024 Elsevier B.V.. All rights reserved.
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    Hybrid Approach for Handling Class Imbalance on Medical Data
    (Institute of Electrical and Electronics Engineers Inc., 2024) Sujay, J.K.; Surakshith, D.T.; Uday, T.Y.; Sneha, H.R.; Annappa, B.; Sushma, V.
    Class imbalance in medical X-ray image datasets poses a significant challenge for developing accurate machine-learning models. This paper presents a novel 'Integrated Strategy for Addressing Class Imbalance in Medical Image Datasets' aiming to tackle this issue systematically. The proposed approach combines weighted loss functions and an ensemble model comprising a pre-trained DenseNet architecture and a customized model. The methodology is applied to a representative medical image dataset, demonstrating its effectiveness in mitigating class imbalance issues. The findings reveal notable improvements in model performance, particularly in underrepresented classes. This research advances robust machine learning models in medical image analysis, with potential applications in medical imaging and illness diagnostics. The results underscore the necessity for hybrid approaches and highlight the efficacy of ensemble models and weighted loss in addressing class imbalance in medical imaging datasets. © 2024 IEEE.